Method and system for modeling predictive outcomes of arthroplasty surgical procedures

ABSTRACT

An apparatus includes a processor and a non-transitory memory. The processor is configured to receive pre-operative patient specific data. The pre-operative patient specific data is inputted to a first machine learning model to determine a first predicted post-operative joint performance data output including first predicted post-operative outcome metrics. A reconstruction plan of the joint of the patient is generated based on a medical image of the joint, and at least one arthroplasty surgical parameter obtained from the user. The at least one arthroplasty surgical parameter is inputted into a second machine learning model to determine a second predicted post-operative joint performance data output including second predicted post-operative outcome metrics. The second predicted post-operative joint performance data output is updated to include an arthroplasty surgery recommendation, in response to the user varying the at least one arthroplasty surgical parameter, before the arthroplasty surgery, during the arthroplasty surgery, or both.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of commonly owned, co-pending U.S.Pat. Application No. 17/233,152., entitled “METHOD AND SYSTEM FORMODELING PREDICTIVE OUTCOMES OF ARTHROPLASTY SURGICAL PROCEDURES,”having a filing date of Apr. 16, 2021, which claims the benefit ofcommonly owned, co-pending U.S. Provisional Pat. Application No.63/011,871, entitled “MACHINE LEARNING TECHNIQUES TO PREDICT CLINICALOUTCOMES AFTER SHOULDER ARTHROPLASTY,” having a filing date of Apr. 17,2020, the contents of which are incorporated by reference herein intheir entirety.

FIELD

The present disclosure relates to machine learning modeling for medicalapplications, and more specifically to method and system for modelingpredictive outcomes of arthroplasty surgical procedures.

BACKGROUND

Supervised machine learning is a class of artificial intelligence bywhich the computer learns the complex structure and relationships inlarge datasets to create predictive models with the help of labeledfeatures. The machine learning model iteratively learns using thefeature data to minimize predictive error. There are numerous commercialapplications of various machine learning techniques.

SUMMARY

In some embodiments, the present disclosure provides an exemplarytechnically improved computer-based apparatus that includes at least thefollowing components of a processor and a non-transitory memory storinginstructions which, when executed by the processor, cause the processorto receive pre-operative patient specific data for an arthroplastysurgery to be performed on a joint of a patient, where the pre-operativepatient specific data may include a medical history of the patient, ameasured range of movement for at least one type of joint movement ofthe joint, and at least one pain metric associated with the joint, toinput the pre-operative patient specific data to at least one firstmachine learning model to determine a first predicted post-operativejoint performance data output, where the first predicted post-operativejoint performance data output may include at least one first predictedpost-operative outcome metric of the joint, to display the firstpredicted post-operative joint performance data output on a display to auser, to receive at least one medical image of the joint obtained fromat least one medical imaging procedure performed on the patient, togenerate a reconstruction plan of the joint of the patient based on theat least one medical image of the joint, and at least one arthroplastysurgical parameter obtained from the user in response to the displayedfirst predicted post-operative joint performance data output, where thereconstruction plan may include the at least one arthroplasty surgicalparameter that is selected from at least one implant, at least oneimplant size, at least one arthroplasty surgical procedure, at least oneposition for implanting the at least one implant in the joint, or anycombination thereof, to input the at least one arthroplasty surgicalparameter into at least one second machine learning model to determine asecond predicted post-operative joint performance data output includingat least one second predicted post-operative outcome metric of thejoint, to display the second predicted post-operative joint performancedata output on the display to the user, and to update the displayedsecond predicted post-operative joint performance data output to includeat least one arthroplasty surgery recommendation, in response to theuser varying any of the at least one arthroplasty surgical parameterbefore the arthroplasty surgery, during the arthroplasty surgery, orboth.

In some embodiments, the present disclosure provides an exemplarytechnically improved computer-based method that includes at least thefollowing steps of receiving, by a processor, pre-operative patientspecific data for an arthroplasty surgery to be performed on a joint ofa patient. The pre-operative patient specific data may include a medicalhistory of the patient, a measured range of movement for at least onetype of j oint movement of the joint, and at least one pain metricassociated with the joint. The pre-operative patient specific data maybe inputted by the processor to at least one first machine learningmodel to determine a first predicted post-operative joint performancedata output. The first predicted post-operative joint performance dataoutput may include at least one first predicted post-operative outcomemetric of the joint. The first predicted post-operative jointperformance data output may be displayed by the processor on a displayto a user. At least one medical image of the joint obtained from atleast one medical imaging procedure performed on the patient may bereceived by the processor. A reconstruction plan of the joint of thepatient may be generated by the processor based on the at least onemedical image of the joint, and at least one arthroplasty surgicalparameter obtained from the user in response to the displayed firstpredicted post-operative joint performance data output. Thereconstruction plan may include the at least one arthroplasty surgicalparameter that is selected from at least one implant, at least oneimplant size, at least one arthroplasty surgical procedure, at least oneposition for implanting the at least one implant in the joint, or anycombination thereof. The at least one arthroplasty surgical parametermay be inputted by the processor into at least one second machinelearning model to determine a second predicted post-operative jointperformance data output comprising at least one second predictedpost-operative outcome metric of the j oint. The second predictedpost-operative joint performance data output may be display by theprocessor on the display to the user. The displayed second predictedpost-operative joint performance data output may be updated by theprocessor to include at least one arthroplasty surgery recommendation,in response to the user varying any of the at least one arthroplastysurgical parameter before the arthroplasty surgery, during thearthroplasty surgery, or both.

DRAWINGS

Some embodiments of the disclosure are herein described, by way ofexample only, with reference to the accompanying drawings. With specificreference now to the drawings in detail, it is stressed that theembodiments shown are by way of example and for purposes of illustrativediscussion of embodiments of the disclosure. In this regard, thedescription taken with the drawings makes apparent to those skilled inthe art how embodiments of the disclosure may be practiced.

FIG. 1 is a block diagram of a system for modeling predictive outcomesof arthroplasty surgical procedures in accordance with one or moreembodiments of the present disclosure;

FIG. 2 is a graph illustrating a preoperative range of motion (ROM)score versus preoperative outcome scores comparing preoperative outcomesof anatomic total shoulder arthroplasty (aTSA) patients in a clinicaloutcome database who would later after their procedure go on to describethemselves as “Much Better” or “Worse” in accordance with one or moreembodiments of the present disclosure;

FIG. 3 is a graph illustrating a preoperative range of motion (ROM)score versus a preoperative outcome score comparing preoperativeoutcomes of reverse total shoulder arthroplasty (aTSA) patients in aclinical outcome database who would later after their procedure go on todescribe themselves as “Much Better” or “Worse” in accordance with oneor more embodiments of the present disclosure;

FIG. 4 is a graph illustrating an age at surgery distribution foranatomic total shoulder arthroplasty (aTSA) patients and reverse totalshoulder arthroplasty (rTSA) in accordance with one or more embodimentsof the present disclosure;

FIG. 5 is a table showing minimally clinically important difference(MCID) and substantial clinical benefit (SCB) thresholds for eachoutcome metric for the overall cohort, aTSA, and rTSA, in accordancewith one or more embodiments of the present disclosure;

FIG. 6 is a table showing a comparison of Mean Absolute Error (MAE)associated with American Shoulder and Elbow Surgeons Shoulder Score(ASES) Prediction Models in accordance with one or more embodiments ofthe present disclosure;

FIG. 7 is a table showing a comparison of Mean Absolute Error (MAE)associated with University of California, Los Angeles (UCLA) PredictionModels in accordance with one or more embodiments of the presentdisclosure;

FIG. 8 is a table showing a comparison of Mean Absolute Error (MAE)associated with Constant Prediction Models in accordance with one ormore embodiments of the present disclosure;

FIG. 9 is a table showing a comparison of Mean Absolute Error (MAE)associated with Global Shoulder Function Score Prediction Models inaccordance with one or more embodiments of the present disclosure;

FIG. 10 is a table showing a comparison of Mean Absolute Error (MAE)associated with visual analogue scale (VAS) Pain Score Prediction Modelsin accordance with one or more embodiments of the present disclosure;

FIG. 11 is a table showing a comparison of Mean Absolute Error (MAE)associated with Active Abduction Prediction Models in accordance withone or more embodiments of the present disclosure;

FIG. 12 is a table showing a comparison of Mean Absolute Error (MAE)associated with Active Forward Elevation Prediction Models in accordancewith one or more embodiments of the present disclosure;

FIG. 13 is a table showing a comparison of Mean Absolute Error (MAE)associated with Active External Rotation Prediction Models in accordancewith one or more embodiments of the present disclosure;

FIG. 14 is a table showing a comparison of the top five most-predictivefeatures as identified by an XGBoost machine learning algorithm topredict patient reported outcome measures (PROM) as ranked by F-score inaccordance with one or more embodiments of the present disclosure;

FIG. 15 is a table showing a comparison of the five most-predictivefeatures as identified by an XGBoost machine learning algorithm topredict pain, function, and ROM as ranked by F-score in accordance withone or more embodiments of the present disclosure;

FIG. 16 is a table showing a comparison of the accuracy of an XGBoostAlgorithm to predict aTSA and rTSA Patients that experienced a clinicalimprovement exceeding the MCID threshold for each of the ASES, UCLA, andConstant Scores in accordance with one or more embodiments of thepresent disclosure;

FIG. 17 is a table showing a comparison of the accuracy of an XGBoostAlgorithm to predict aTSA and rTSA Patients that experienced a clinicalimprovement exceeding the MCID threshold for each of the Global ShoulderFunction and VAS Pain Scores for Active Abduction, Forward Elevation,and External Rotation ROM Measures in accordance with one or moreembodiments of the present disclosure;

FIG. 18 is a table showing a comparison of the accuracy of an XGBoostAlgorithm to predict aTSA and rTSA Patients that experienced a clinicalimprovement exceeding the SCB threshold for each of the ASES, UCLA, andConstant Scores in accordance with one or more embodiments of thepresent disclosure;

FIG. 19 is a table showing a comparison of the accuracy of an XGBoostAlgorithm to predict aTSA and rTSA Patients that experienced a clinicalimprovement exceeding the SCB threshold for each of the Global ShoulderFunction and VAS Pain Scores, and for Active Abduction, ForwardElevation, and External Rotation ROM Measures in accordance with one ormore embodiments of the present disclosure;

FIG. 20 is a table showing a list of predictive model inputs to machinelearning models for calculating the Global Shoulder Function Score, theVAS Pain Score, and Active Abduction, Active Forward Elevation, andActive External Rotation in accordance with one or more embodiments ofthe present disclosure;

FIG. 21 is a table showing a list of additional predictive model inputs(over the inputs presented in FIG. 20 ) to machine learning models forcalculating an ASES score in accordance with one or more embodiments ofthe present disclosure;

FIG. 22 is a table showing a list of additional predictive model inputs(over the inputs presented in FIG. 20 ) to machine learning models forcalculating a Constant Score in accordance with one or more embodimentsof the present disclosure;

FIG. 23 is an exemplary flow diagram for modeling predictive outcomes ofarthroplasty surgical procedures in accordance with one or moreembodiments of the present disclosure;

FIG. 24 is a table showing a comparison of Mean Absolute Error (MAE)associated with the ASES predictions using the Full and AbbreviatedXGBoost machine learning models in accordance with one or moreembodiments of the present disclosure;

FIG. 25 is a table showing a comparison of Mean Absolute Error (MAE)associated with the constant predictions using the Full and AbbreviatedXGBoost machine learning models in accordance with one or moreembodiments of the present disclosure;

FIG. 26 is a table showing a comparison of Mean Absolute Error (MAE)associated with the Global Shoulder Function Score Predictions using theFull and Abbreviated XGBoost machine learning models in accordance withone or more embodiments of the present disclosure;

FIG. 27 is a table showing a comparison of Mean Absolute Error (MAE)associated with the VAS Pain Score Predictions using the Full andAbbreviated XGBoost machine learning models in accordance with one ormore embodiments of the present disclosure;

FIG. 28 is a table showing a comparison of Mean Absolute Error (MAE)associated with the Active Abduction Predictions using the Full andAbbreviated XGBoost machine learning models in accordance with one ormore embodiments of the present disclosure;

FIG. 29 is a table showing a comparison of Mean Absolute Error (MAE)associated with the Active Forward Elevation Predictions using the Fulland Abbreviated XGBoost machine learning models in accordance with oneor more embodiments of the present disclosure;

FIG. 30 is a table showing a comparison of Mean Absolute Error (MAE)associated with the Active External Rotation Predictions using the Fulland Abbreviated XGBoost machine learning models in accordance with oneor more embodiments of the present disclosure;

FIG. 31 is a table showing a comparison of a full XGBoost modelpredictions for aTSA and rTSA patients that experienced a clinicalimprovement exceeding the MCID threshold for multiple different outcomemeasures in accordance with one or more embodiments of the presentdisclosure;

FIG. 32 is a table showing a comparison of an abbreviated XGBoost modelpredictions for aTSA and rTSA patients that experienced a clinicalimprovement exceeding the MCID threshold for multiple different outcomemeasures in accordance with one or more embodiments of the presentdisclosure;

FIG. 33 is a table showing a comparison of a full XGBoost modelpredictions for aTSA and rTSA patients that experienced a clinicalimprovement exceeding the SCB threshold for multiple different outcomemeasures in accordance with one or more embodiments of the presentdisclosure;

FIG. 34 is a table showing a comparison of an abbreviated XGBoost modelpredictions for aTSA and rTSA patients that experienced a clinicalimprovement exceeding the SCB threshold for multiple different outcomemeasures in accordance with one or more embodiments of the presentdisclosure;

FIG. 35 is a table showing a comparison of an abbreviated XGBoost modelwith inputs from CT planning data to make predictions for aTSA and rTSApatients that experienced a clinical improvement exceeding the MCIDthreshold for multiple different outcome measures in accordance with oneor more embodiments of the present disclosure;

FIG. 36 is a table showing a comparison of an abbreviated XGBoost modelwith inputs from CT planning data to make predictions for aTSA and rTSApatients that experienced a clinical improvement exceeding the SCBthreshold for multiple different outcome measures in accordance with oneor more embodiments of the present disclosure; and

FIG. 37 is a flowchart of an exemplary method for modeling predictiveoutcomes of arthroplasty surgical procedures in accordance with one ormore embodiments of the present disclosure.

DETAILED DESCRIPTION

Among those benefits and improvements that have been disclosed, otherobjects and advantages of this disclosure will become apparent from thefollowing description taken in conjunction with the accompanyingfigures. Detailed embodiments of the present disclosure are disclosedherein; however, it is to be understood that the disclosed embodimentsare merely illustrative of the disclosure that may be embodied invarious forms. In addition, each of the examples given regarding thevarious embodiments of the disclosure which are intended to beillustrative, and not restrictive.

Throughout the specification and claims, the following terms take themeanings explicitly associated herein, unless the context clearlydictates otherwise. The phrases “in one embodiment,” “in an embodiment,”and “in some embodiments” as used herein do not necessarily refer to thesame embodiment(s), though it may. Furthermore, the phrases “in anotherembodiment” and “in some other embodiments” as used herein do notnecessarily refer to a different embodiment, although it may. Allembodiments of the disclosure are intended to be combinable withoutdeparting from the scope or spirit of the disclosure.

As used herein, the term “based on” is not exclusive and allows forbeing based on additional factors not described, unless the contextclearly dictates otherwise. In addition, throughout the specification,the meaning of “a,” “an,” and “the” include plural references. Themeaning of “in” includes “in” and “on.”

As used herein, terms such as “comprising” “including,” and “having” donot limit the scope of a specific claim to the materials or stepsrecited by the claim.

All prior patents, publications, and test methods referenced herein areincorporated by reference in their entireties.

EXAMPLES

Variations, modifications and alterations to embodiments of the presentdisclosure described above will make themselves apparent to thoseskilled in the art. All such variations, modifications, alterations andthe like are intended to fall within the spirit and scope of the presentdisclosure, limited solely by the appended claims.

While several embodiments of the present disclosure have been described,it is understood that these embodiments are illustrative only, and notrestrictive, and that many modifications may become apparent to those ofordinary skill in the art. For example, all dimensions discussed hereinare provided as examples only, and are intended to be illustrative andnot restrictive.

Any feature or element that is positively identified in this descriptionmay also be specifically excluded as a feature or element of anembodiment of the present as defined in the claims.

Machine learning techniques for healthcare applications offer thepotential to transform complex healthcare data into practical knowledgethat can help surgeons better understand their patients and thecomplexities of their patient’s conditions. By leveraging largequantities of high-quality clinical outcomes data, machine learninganalyses can identify previously hidden correlations and relationshipsin datasets to create predictive models that can better informindividual patient treatment decisions.

In orthopedics, predictive models derived from high-quality outcomes andpatient data may represent a patient-specific implementation ofevidence-based decision-making tools, that may transform complexhealthcare data into practical knowledge to support more-informedtreatment decision making. While the commercial usage of machinelearning may be new to orthopedics, its usage in research has increasedin recent years. Many machine learning applications have beenimage-based analyses, but there is a growing interest to use machinelearning techniques to predict clinical outcomes. Predictive outcomesmodels may assist the orthopedic surgeon to better identify whichpatients will benefit from elective procedures, such as arthroplasty,and also help better-align patient and surgeon expectations for clinicalimprovement by leveraging the experiences of previous patients withsimilar demographics, diagnoses, comorbidities, clinical history, andtreatments. With more insight into the factors that predictpatient-specific improvement, and with better alignment betweenpredicted and actualized outcomes, patient satisfaction levels maylikely increase through the use of such an evidenced-based predictiveoutcomes tool.

Embodiments of the present disclosure herein describe methods andsystems for modeling predictive outcomes of arthroplasty surgicalprocedures. Arthroplasty may be used to repair or replace any joint inthe body, including but not limited to the hips, knees, shoulders,elbows, and ankles, for example. However, to further illustrate thesemethods and systems, shoulder arthroplasty is used herein as anexemplary embodiment throughout this disclosure.

FIG. 1 is a block diagram of a system 10 for modeling predictiveoutcomes of arthroplasty surgical procedures in accordance with one ormore embodiments of the present disclosure. The system 10 may include aserver 15, a medical imaging system 35, a plurality of N electronicmedical resources denoted ELECTRONIC RESOURCE1 40A....ELECTRONICRESOURCEN 40B, where N is an integer, and a computing device 77 of auser 20 all communicating 32 over a communication network 30. Thecomputing device 77 of the user 20 may also be communicatively coupled37 directly to the server 15.

In some embodiments, the user 20, that may interact with a graphic userinterface (GUI) 75 on the computing device 77, may be a physiciandiscussing an arthroplasty surgical procedure to be performed on apatient 25. In other embodiments, the computing device 77 may be placedin any suitable location such as an operating room where the jointarthroplasty surgical procedure may be performed.

The server 15 may include a processor 45, a non-transitory memory 60, acommunication circuitry 70 for communicating 32 over the communicationnetwork 30, and/or I/O devices 65, such as a display for displaying theGUI 75 to the user 20, a keyboard 65A and a mouse 65B, for example.

In some embodiments, the server 15 may be configured to executedifferent software modules to perform the functions in the system 10 asdescribed herein. The different software modules may include, but arenot limited to, a patient-specific data collection module 46, a computedtomography (CT) image-based guided personalized surgery (GPS) JointReconstruction Planning module 48, an initial pre-op prediction machinelearning model (MLM) module 50, an image-based Prediction MLM module 52,a machine learning model training module 54, and a GUI manager module 56for controlling the GUI 75 on the user’s computing device 77.

In some embodiments, the non-transitory memory 60 may be configured tostore a clinical outcome database 62 with a plurality of clinicaloutcomes of different types of arthroplasty surgical proceduresperformed on a plurality of patients.

In some embodiments, the patient-specific data collection module 46 mayquery any of the plurality of electronic medical resources 40A and 40Bover the communication network 30 to obtain medical data from thepatient 25. The plurality of electronic medical resources 40A and 40Bmay be managed by the patient’s health management organization HMO, ahospital that the patient 25 received medical treatment, a doctor thatthe patient 25 received medical treatment, for example.

In some embodiments, the CT image-based GPS Joint Reconstruction Planmodule 48 may analyze the data from medical images received from themedical imaging system 35. The medical imaging system 35 may generate anX-ray image, a computed tomography (CT) image, a magnetic resonanceimage, and/or a three-dimensional (3D) medical image, for example. The3D medical image may be generated from a plurality of X-ray images. Themedical image may include a frame from a video of the joint.

In some embodiments, the machine learning model (MLM) training module 54may generate a training dataset for training the machine learning modelsused in the system 10. For example, MLM training module 54 may retrievepatient outcome data from the clinical outcome database 62 to generate adataset that maps, in part, data vectors of pre-operative patientspecific data and arthroplasty surgical parameters used in differenttypes of arthroplasty surgical procedures to known post-operativeoutcome metrics of the joint replacement. The trained machine learningmodels may then generate predicted post-operative outcome metrics of thejoint replacement given the input data vectors for a new patient priorto arthroplasty surgery.

In some embodiments, with regard to shoulder arthroplasty, machinelearning techniques may be used to pre-operatively predict clinicaloutcomes at various post-operative timepoints after surgery for patientsreceiving total shoulder arthroplasty. These predictions may be used toinform the shoulder surgeon of what a particular patient may expect toexperience after anatomic total shoulder arthroplasty (aTSA) and/orreverse total shoulder arthroplasty (rTSA), for example. A list of modelinputs may be obtained from the health care professional and/orautomatically from the patient’s electronic medical record throughsoftware integration by querying any of the electronic medical resourcesas previously described. While this disclosure focuses on aTSA and rTSAoutcomes prediction, these models could also be applied to othershoulder arthroplasty applications, like hemiarthroplasty, fracturereconstruction, endoprostheses, resurfacing, and primary vs. revisionarthroplasty outcomes predictions.

In some embodiments, regarding the inputs to these predictive models,the predictive outcomes for a given total shoulder arthroplasty patientmay be further refined to provide recommendations for optimal clinicaloutcomes using different implant sizes, such as different sizes ofhumeral heads, humeral stems, glenospheres, glenoid or humeral augments,for example, implant types, such as aTSA, rTSA, hemiarthroplasty,resurfacing, short stem, stemless, fracture arthroplasty,endoprostheses, revision devices, for example, and/or surgicaltechniques, such as delto-pectoral, superior-lateral, subscapularissparing, for example, in order to account for the patient specificdiagnoses and bone/soft tissue morphological considerations.

In some embodiments, using machine learning predictive outcomealgorithms to pre-operatively predict a patient’s post-operativeclinical outcomes may have numerous additional practical applicationsthat are valuable to the patient and surgeon. First, the ability todifferentiate pre-operatively which patients may achieve clinicalimprovement after aTSA and rTSA relative to patient satisfactionanchor-based minimal clinically important difference (MCID) andsubstantial clinical benefit (SCB) thresholds for multiple differentpatient reported outcome measures (PROMs) and active range of motion(ROM) measurements may be useful to the orthopedic surgeon toobjectively identify which patients are appropriate candidates for theseelective procedures. It may also assist the orthopedic surgeon to decidebetween implant types for a particular patient. As a non-operativetreatment may be best for some patients, this foreknowledge mayrepresent a more efficient resource allocation for the patient, surgeon,hospital, and/or payer.

In this disclosure, the terms “outcome metric” and “outcome measure” maybe used interchangeably herein. The terms “machine learning model”,“machine learning module”, “machine learning predictive outcomealgorithms”, “predictive outcome algorithms” and/or “predictive outcomemodel” may be used interchangeably herein.

In some embodiments, a patient-specific prediction of clinicalimprovement at multiple post-surgical timepoints may be helpful to alignpatient and surgeon expectations on what is achievable after thiselective procedure. Given the association between pre-operativeexpectations and post-operative satisfaction, better surgeon-patientalignment on both the magnitude and rate of clinical improvement mayresult in greater levels of patient satisfaction. Furthermore, animproved understanding of the amount of clinical improvement that can beexpected at different post-surgical timepoints for a given patient mayaid the surgeon in establishing protocols for rehabilitation. This mayalso help both the surgeon and patient weigh these gains versus theprocedure-specific risks associated with aTSA and rTSA, such as:instability, aseptic loosening, and infection.

In some embodiments, the machine learning techniques disclosed hereinmay be extended to predict outcomes and improvement based upon specificdiagnoses and to also predict and/or identify patients with risk factorsfor various complications. Furthermore, the predictive models may helpappropriately risk-stratify patients and make recommendations onhealthcare workflows, such as identifying patients that may safely havesurgery in an ambulatory surgical center or patients that should have anin-patient vs. outpatient surgery in a hospital. The predictive modelsmay make recommendations for a specific patient on their duration lengthfor hospital stay after the arthroplasty procedure.

In some embodiments, the predictive models may provide a betterunderstanding of the factors influencing outcomes, which may assist theorthopedic surgeon to personalize care for each patient in terms ofpatient-specific requirements for pain relief, function, and mobility,as well as to help the patient better understand how well thearthroplasty surgical procedure may meet their needs based upon thatpatients unique characteristics that are input to and accounted for inthe predictive model output.

FIG. 2 is a graph illustrating a preoperative range of motion (ROM)score versus preoperative outcome scores comparing preoperative outcomesof anatomic total shoulder arthroplasty (aTSA) patients in a clinicaloutcome database who would later after their procedure go on to describethemselves as “Much Better” or “Worse” in accordance with one or moreembodiments of the present disclosure.

FIG. 3 is a graph illustrating a preoperative range of motion (ROM)score versus a preoperative outcome score comparing preoperativeoutcomes of reverse total shoulder arthroplasty (aTSA) patients in aclinical outcome database who would later after their procedure go on todescribe themselves as “Much Better” or “Worse” in accordance with oneor more embodiments of the present disclosure;

Note that in both FIGS. 2 and 3 , the preoperative outcomes of aTSA andrTSA patients in the clinical outcome database 62 may be based on aTSAand rTSA patients that post-operatively rate themselves as “much better”versus those who rate themselves as “worse” during the latestpost-operative follow-up. Note the relative equal distribution ofpatients between the two cohorts in both FIGS. 2 and 3 indicates thatthese patients may be difficult to identify by the orthopedic surgeonand distinguish based upon these parameters alone prior to surgery if aparticular patient would have a “much better” or “worse” outcome, if thepatient were to have a given procedure.

In some embodiments, evidence-based pre-operative predictive outcomestool greatly assist surgeons objectively to establish patient-specificgains that will be achieved after arthroplasty because it is typicallydifficult for arthroplasty surgeons to pre-operatively identify as towhich patients will achieve poor outcomes and which patients will bedissatisfied with the procedure based upon the currently availableknowledge and clinical guidelines, and known risk factors.

Positive outcomes may be common for patients after total shoulderarthroplasty with about 90% of patients reporting that they aresatisfied with their procedure (e.g., patients stating they are “better”or “much better” relative to their non-operative condition) for patientsreceiving aTSA and/or rTSA, as compared to patients who are unsatisfied(e.g., patients stating they are “unchanged” or “worse” relative totheir non-operative condition). However, the predictability of patientswho will achieve these poor outcomes may be less certain for both aTSA(FIG. 2 ) and rTSA (FIG. 3 ) as demonstrated by the presentation ofpreoperative outcomes as compared to patients who would go on to be“much better” as compared to those who would go on to be “worse”post-operatively.

Predictability of outcomes after total shoulder arthroplasty may be lesscertain when considering improvements in function and the amount ofrange of motion the patient will achieve in a given plane at aparticular time of follow-up after surgery. For example, most shouldersurgeons may consider that improvement of active rotation and the amountof active rotation after rTSA is unpredictable so that they may notaccurately advise patients if they will improve their ability toactively rotate their arm or not.

With regard to the recovery time for a patient to regain a full range ofmotion after total shoulder arthroplasty as well as full outcomes asmeasured by various patient reported outcome measures for measuring(PROMs: e.g. ASES, Constant, UCLA, Shoulder Function, Simple ShoulderTest (SST), Shoulder Pain And Disability Index (SPADI), VAS Pain,Shoulder Arthroplasty Smart Score, etc), the majority of improvementthat a patient may experience is typically achieved within the first 6months after the arthroplasty procedure. However, some patients may takeas long as 2 years after the procedure to achieve the full range ofmotion or to obtain a maximum PROM score.

Additionally, the full range of motion and/or maximum PROM score mayvary between patients, due to many different factors, including, forexample, but not limited to patient demographics, comorbidities,diagnosis, severity of diagnosis/degenerative condition, bone/softtissue quality, bone morphology, implant selection type, implant sizing,implant positioning, and/or surgical technique information. Thus,surgeon and patient expectations may not be accurate and may fail toalign due to all of these above-mentioned factors, that may lead toincreased dissatisfaction with the procedure. Thus, there exists a needto better and more accurately predict outcomes as defined by PROMs andROM after total shoulder arthroplasty, taking into account all possiblevariables in order to better help patients and surgeons achieve moreaccurate expectations, improved predictability, and improvedsatisfaction.

FIG. 4 is a graph illustrating an age at surgery distribution foranatomic total shoulder arthroplasty (aTSA) patients and reverse totalshoulder arthroplasty (rTSA) in accordance with one or more embodimentsof the present disclosure. FIG. 4 illustrates that older patients may bemore likely to receive a rTSA procedure than an aTSA procedure, whileyounger patients may be more likely to received aTSA. The cross-over ageby which patients are more likely to receive a rTSA is 64 years of ageat the time of surgery. For patients older than 75 years of age at thetime of surgery, the ratio is 4:1 for rTSA as compared to aTSA.

Additionally, due to a recent blending of indications between aTSA andrTSA, and also a recent shift in trends by shoulder surgeons to increasetheir usage of rTSA for older patients to mitigate the occurrences ofrotator cuff related complications, which predominately occur with aTSA,and not rTSA, as shown in FIG. 4 , there is a need to help surgeonsbetter predict which arthroplasty procedure would provide betteroutcomes.

The embodiments herein describe a method, workflow, and computersoftware system as shown in the system 10 of FIG. 1 that predictsoutcomes and range of motion of joints having underwent afterarthroplasty surgical procedures using a multivariable-based machinelearning analysis of outcomes data from the clinical outcome database 62(e.g., that may be used for training the machine learning predictivemodel implemented herein). Thus, the trained machine learning predictivemodels may extrapolate those statistical trends and relationships tothat of patient-specific data for a particular patient who would receivejoint arthroplasty in order to more accurately predict prior to surgery,the post-operative outcome measures that particular patient may achieve.

In some embodiments, the surgeon may utilize this predictive modelderived information to help identify outcomes as measured by multipledifferent outcome metrics at various post-surgical timepoints forvarious implant types and sizes and to also compare those predictedresults to other similar patients from the clinical outcome database 62so as to extrapolate their outcomes based upon the experiences of othersimilar patients.

In some embodiments, the predictive models may be used to compare rangeof outcomes achieved with different implant types (such as aTSA vs. rTSAfor shoulder arthroplasty), different implant sizes, and differentimplant positions as compared to other patients in the clinical outcomedatabase 62 for various defined diagnoses, comorbidities, bonedeformities, and/or soft-tissue conditions within the joint underconsideration. All of these considerations may be used to establish andcommunicate more accurate expectations of actual results, and bettersurgeon-to-patient alignment.

In some embodiments, the predictive model may utilize data from theclinical outcome database 62 to identify the complex interactions inthis data, classify the data, and/or identify the most importantcontributors and associations to post-operative outcomes. Thesepredictive algorithms may further model and predict post-operativeresults for similar new cases for various different PROMs and range ofmotion measures. Each of the predictive models may be analyzed aloneand/or concatenated in a series where the results of one predictivemodel may be an input to another new predictive model.

In some embodiments, the predictive models for total shoulderarthroplasty generated in for the exemplary embodiments shown in thisdisclosure were trained using data from the clinical outcome database 62from more than 8,000 patients and 20,000 post-operative patient visits.There were about 300 pre-operative data inputs for each patient on whichto base the analysis. This predictive analysis may perform a regressionanalysis, a deep-learning based analysis, at least one ensemble-baseddecision tree learning method, or any combination thereof, so as tocombine outcomes from multiple various decision trees to identify andrank the pre-operative parameters that most significantly relate tooutcomes with total shoulder arthroplasty.

In some embodiments, by identifying and ranking these parameters as wellas the most-relevant risk factors out of data related to patientdemographics, comorbidities, diagnosis, severity ofdiagnosis/degenerative condition, bone/soft tissue quality, bonemorphology, implant selection type, implant sizing, implant positioning,and/or surgical technique information, for example, the predictivemodels may aid the surgeon to provide the best outcomes possible for aparticular patient by leveraging this large database of clinicalhistory. The predictive models may provide actionable recommendations tothe surgeon in identifying and communicating these complex interactionsbetween these parameters.

In some embodiments, the system 10 by which the predictive models may beaccessed on the computing device 77 by the surgeon 20 may be apre-operative planning software, that provides recommendations on whichimplant types and implant sizes that the surgeon may select and providerecommendations for where these implants should be positioned.

In some embodiments, the system 10 by which the predictive models may beaccessed on the computing device 77 by the surgeon 20 may provide a GUI75 for an intra-operative computer navigation or robotic system whichpermits on the fly changes to the pre-operative plan based uponintra-operative findings by the surgeon and/or hospital staff (e.g.,during the surgical procedure). Each of the aforementioned actionableguidance may be communicated by the predictive models intra-operatively(e.g. implant type, implant size, and/or implant position). Conversely,the predictive model may be accessed via a stand-alone softwareapplication available on multiple different software platforms which maybe accessible to the patient, surgeon, or other healthcare professional.

In some embodiments, three supervised machine learning techniques may beused including a linear-regression-based, a tree-based, and/or adeep-learning-based machine learning, to analyze data on the clinicaloutcome database 62 of shoulder arthroplasty patients who received asingle platform shoulder prosthesis (see, for example, Equinoxe,Exactech Inc., Gainesville, FL) between November 2004 and December 2018.Every shoulder arthroplasty patient consented to data sharing and alldata was collected using standardized forms according to anInstitutional Review Board (IRB)-approved protocol.

In some embodiments, to ensure a homogenous dataset, patients withrevisions, a diagnosis of humeral fracture, and hemiarthroplasty caseswere excluded. Patients with a less than 3 months follow-up were alsoexcluded. These criteria may result in pre-operative, intra-operative,and post-operative data from 5,774 patients with 17,427 post-operativefollow-up visits available to train and generate algorithms that predictpost-operative scores of the: ASES, UCLA, and Constant metrics, theglobal shoulder function score (0=no mobility and 10=normal), the VASpain score (0=no pain and 10=extreme pain), active abduction (0°-180°arm elevation in the frontal plane), active forward elevation (0°-180°arm elevation in the sagittal plane), and/or active external rotation(-90 to 90° with the arm at the side) at 3-6 months, 6-9 months, 1 year[9-18 months], 2-3 years [18-36 months], 3-5 years [36-60 months], and5+ years [60+ months]. An active range of motion was measured with agoniometer at each patient clinical visit.

In some embodiments, the predictive algorithms may be trained andgenerated using demographic data, diagnoses, comorbidities, implanttype, pre-operative ROM, pre-operative radiographic findings, andpre-operative PROM scores (such as the ASES, SPADI, SST, UCLA, andConstant metrics), including the individual questions used to deriveeach score; in total, 291 labeled features were utilized. The clinicaldata from 2,153 primary aTSA patients (7,305 visits; averagefollow-up=26.7 months) and 3,621 primary rTSA patients (10,122 visits;average follow-up=22.8 months) was used to train and generate thepredictive models at each post-surgical timepoint: 3-6 months (aTSA=1282and rTSA=2227 visits), 6-9 months (aTSA=658 and rTSA=1177 visits), 1year (aTSA=1451 and rTSA=2445 visits), 2-3 years (aTSA=1347 andrTSA=1882 visits), 3-5 years (aTSA=1321 and rTSA=1482 visits), and 5+years (aTSA=1246 and rTSA=907 visits). A random selection of 66.7% ofthis data defined the training cohort and the remaining 33.3% definedthe validation test cohort, which was used to evaluate the predictionerror of each algorithm.

In some embodiments, the predictive models may include three trainedsupervised machine learning techniques: 1) linear regression, 2)XGBoost, and 3) Wide and Deep.

As a general technical background to these predictive models, a linearregression model assumes and models a linear relationship between thepre-operative data (input variables) and the outcomes data (outputvariable) from the full training dataset. An XGBoost model is anensemble method of multiple regression-trees. These regression-trees maybe built by iteratively partitioning the entire training dataset intomultiple small batches using a method called boosting. XGBoost mayhandle missing-values and data-sparsity relatively well. The Wide andDeep model is a hybrid of the linear regression model and adeep-learning model that is particularly useful for classificationproblems with sparse inputs. The features in the clinical outcomedatabase 62 may be categorical, so the Wide and Deep model may be wellsuitable to this technique.

In some embodiments, the deep-learning component may utilize a layeredfunction that computes the model coefficients based upon inputs from aprevious layer, ultimately propagating those coefficients to thetop-layer of the outcome prediction model. The wide (or linearcomponent) may be used for dense/numeric features while the deep (orfeed-forward neural network component) may used for sparse/categoricalfeatures. A baseline average analysis as the study control may be usedto evaluate the relative accuracy of each predictive model.

FIG. 5 is a table showing minimally clinically important difference(MCID) and substantial clinical benefit (SCB) thresholds for eachoutcome metric (measure) for the overall cohort, aTSA, and rTSA, inaccordance with one or more embodiments of the present disclosure. Theprimary target of each model may be used to predict the post-operativeoutcome measure at each post-surgical timepoint. The secondary targetsmay be identified if a patient would experience clinical improvementgreater than the MCID and SCB patient satisfaction anchor-basedthresholds for each measure previously established by Simovitch et al.referring to FIG. 5 . MCID may represent the floor threshold forimprovement and may define the minimum improvement that a patientperceives as a meaningful change by a given treatment. SCB may differfrom MCID in that it may represent the target level of improvement forachieving a substantial benefit as perceived by the patient.

In some embodiments, the predictive performance of the primary target ofeach model may be quantified by the Mean Absolute Error (MAE) betweenthe actual and predicted values for each outcome measure for aTSA andrTSA patients in the 33.3% validation test cohort. To aid in modelinterpretability, an F-score from the XGBoost model may be used toidentify the most-predictive features. The F-score may quantify thefrequency that a particular feature may be used as a candidate for asplit in the decision-tree algorithm. The performance of the secondarytarget, or the accuracy of each model to identify if a patient willachieve the MCID and SCB improvement thresholds for each outcome measureat 2-3 years follow-up may be quantified using the classificationmetrics of precision for quantifying the ability for a model to notidentify a negative as positive, recall for quantifying the ability fora model to identify a positive as a positive, F1-score for quantifyingthe harmonic mean between the precision and recall scores, accuracy forquantifying the ratio of the correct predictions to the total number ofpredictions, and/or the Area Under the Receiver Operating Curve (AUROC),all of which may determine the overall accuracy of the model. Theresults of these predictive models are tabulated below.

FIG. 6 is a table showing a comparison of Mean Absolute Error (MAE)associated with American Shoulder and Elbow Surgeons Shoulder Score(ASES) Prediction Models in accordance with one or more embodiments ofthe present disclosure.

FIG. 7 is a table showing a comparison of Mean Absolute Error (MAE)associated with University of California, Los Angeles (UCLA) PredictionModels in accordance with one or more embodiments of the presentdisclosure.

FIG. 8 is a table showing a comparison of Mean Absolute Error (MAE)associated with Constant Prediction Models in accordance with one ormore embodiments of the present disclosure.

FIG. 9 is a table showing a comparison of Mean Absolute Error (MAE)associated with Global Shoulder Function Score Prediction Models inaccordance with one or more embodiments of the present disclosure.

FIG. 10 is a table showing a comparison of Mean Absolute Error (MAE)associated with visual analogue scale (VAS) Pain Score Prediction Modelsin accordance with one or more embodiments of the present disclosure.

FIG. 11 is a table showing a comparison of Mean Absolute Error (MAE)associated with Active Abduction Prediction Models in accordance withone or more embodiments of the present disclosure.

FIG. 12 is a table showing a comparison of Mean Absolute Error (MAE)associated with Active Forward Elevation Prediction Models in accordancewith one or more embodiments of the present disclosure.

FIG. 13 is a table showing a comparison of Mean Absolute Error (MAE)associated with Active External Rotation Prediction Models in accordancewith one or more embodiments of the present disclosure.

The primary target predictions for the ASES (FIG. 6 ), UCLA (FIG. 7 )and Constant (FIG. 8 ) PROMs, the global shoulder function score (FIG. 9), VAS pain score (FIG. 10 ), active abduction (FIG. 11 ), forwardelevation (FIG. 12 ), and external rotation (FIG. 13 ) at 1 year, 2-3years, 3-5 years, and 5+ years after aTSA and rTSA as presented in thetables of FIGS. 6-13 . The Wide and Deep model had the lowest MAE forevery measure at each timepoint, followed by XGBoost, and the linearregression model. In spite of accuracy differences, all three predictiveoutcome algorithms had lower MAE than the baseline average model.

Based on the average weighted MAE, each machine learning technique wasmost accurate at predicting the Constant score (±7.56% MAE), followedclosely by the UCLA score (±8.16% MAE), and finally the ASES score(±10.45% MAE). Across all post-surgical timepoints analyzed, the averageMAE for the Wide and Deep prediction model was ±1.2 for the globalshoulder function score, ±1.9 for the VAS pain score, ±19.5° for activeabduction, ±15.9° for forward elevation, and ±11.4° for externalrotation. Differences between aTSA and rTSA patients were similar, withonly minor differences observed between each score, each plane of motionanalyzed, and across post-surgical timepoints. Note that otherpredictive models may be generated using this data and techniques, suchas the internal rotation score, visual analogue scale pain, and/or theshoulder arthroplasty smart score.

FIG. 14 is a table showing a comparison of the top five most-predictivefeatures as identified by an XGBoost machine learning algorithm topredict patient reported outcome measures (PROM) as ranked by F-score inaccordance with one or more embodiments of the present disclosure. FIG.15 is a table showing a comparison of the five most-predictive featuresas identified by an XGBoost machine learning algorithm to predict pain,function, and ROM as ranked by F-score in accordance with one or moreembodiments of the present disclosure.

In some embodiments, the top five most-predictive features utilized bythe XGBoost predictive models for each PROM (FIG. 14 ), and pain,function, and ROM measures (FIG. 15 ) is presented in the tables ofFIGS. 14-15 . In the examples disclosed in this disclosure, for the 291features used, XGBoost predictive models yielded excellent agreement inthe top five F-score-ranked features, though some differences wereobserved between the PROM models and the pain, function, and ROM models.Follow-up duration, representing the amount of recovery time aftersurgery, was identified as the most-predictive feature used in allmodels.

In some embodiments, with regard to the PROMs, two differentpre-operative PROMs (SPADI and ASES) and four different pre-operativemeasures of active ROM were also observed to be highly predictive, alongwith the categorical question: “Is surgery on dominant hand?”.Concerning the pain, function, and ROM measures, the categoricalquestion: “Is surgery on dominant hand?” was identified as the secondmost-predictive feature in all models. The categorical question: “Isgender female?” was identified as the third most-predictive feature inall models but one. Other highly predictive features were: thepre-operative SPADI score, two different pre-operative measures ofactive ROM, and a categorical question: “Did patient have previousshoulder surgery?”.

FIG. 16 is a table showing a comparison of the accuracy of an XGBoostAlgorithm to predict aTSA and rTSA Patients that experienced a clinicalimprovement exceeding the MCID threshold for each of the ASES, UCLA, andConstant Scores in accordance with one or more embodiments of thepresent disclosure.

FIG. 17 is a table showing a comparison of the accuracy of an XGBoostAlgorithm to predict aTSA and rTSA Patients that experienced a clinicalimprovement exceeding the MCID threshold for each of the Global ShoulderFunction and VAS Pain Scores for Active Abduction, Forward Elevation,and External Rotation ROM Measures in accordance with one or moreembodiments of the present disclosure.

FIG. 18 is a table showing a comparison of the accuracy of an XGBoostAlgorithm to predict aTSA and rTSA Patients that experienced a clinicalimprovement exceeding the SCB threshold for each of the ASES, UCLA, andConstant Scores in accordance with one or more embodiments of thepresent disclosure.

FIG. 19 is a table showing a comparison of the accuracy of an XGBoostAlgorithm to predict aTSA and rTSA Patients that experienced a clinicalimprovement exceeding the SCB threshold for each of the Global ShoulderFunction and VAS Pain Scores, and for Active Abduction, ForwardElevation, and External Rotation ROM Measures in accordance with one ormore embodiments of the present disclosure.

In some embodiments, the secondary target MCID predictions for the PROMmodels (FIG. 16 ) and the pain, function, and ROM models (FIG. 17 ) at2-3 years follow-up is presented in the tables of FIGS. 16-17 . TheXGBoost PROM models yielded 93-95% accuracy in MCID with an AUROCbetween 0.87-0.94 for aTSA patients and 93-99% accuracy in MCID with anAUROC between 0.85-0.97 for rTSA patients. In other embodiments, theXGBoost pain/function/ROM models yielded 85-94% accuracy in MCID with anAUROC between 0.79-0.91 for aTSA patients and 90-94% accuracy in MCIDwith an AUROC between 0.78-0.90 for rTSA patients.

In some embodiments, the SCB predictions for the PROM models (FIG. 18 )and the pain, function, and ROM models (FIG. 19 ) at 2-3 years follow-upis presented in the tables of FIGS. 18-19 . The XGBoost PROM modelsyielded 82-90% accuracy in SCB with an AUROC between 0.80-0.90 for aTSApatients and 87-93% accuracy in SCB with an AUROC between 0.81-0.89 forrTSA patients. In other embodiments, the XGBoost pain/function/ROMmodels yielded 76-89% accuracy in SCB with an AUROC between 0.73-0.86for aTSA patients and 88-90% accuracy in SCB with an AUROC between0.77-0.88 for rTSA patients.

In some embodiments, the predictive outcome analysis may demonstrate theefficacy of multiple machine learning techniques to generate models thataccurately predict three PROM scores, pain and function scores, andthree active ROM measures at numerous post-surgical follow-up timepointsfor both aTSA and rTSA. Prediction accuracy for PROMs, pain relief, andfunction were similar between aTSA and rTSA patients at each timepointwere analyzed. The Wide and Deep technique consistently demonstrated thebest overall predictive performance. Most significantly, these modelsmay risk-stratify patients by accurately identifying patients at thegreatest risk for poor outcomes (e.g., failure to achieve MCIDthresholds) and accurately identifying patients most likely to achieveexcellent outcomes (e.g., to achieve SCB thresholds).

However, the use of 291 exemplary variable inputs used in these shoulderarthroplasty examples may not be a practical tool for an orthopedicsurgeon to use in clinic, given the large data-input and time burden onthe surgeon and patient. In a review of the F-score results of thisanalysis and the application of extensive domain knowledge related tototal shoulder arthroplasty, an abbreviated model was generated whichrequires only 10-20% of the original model inputs Thus, a clinicaldeployment of such a software predictive outcome tool may be morepractical for the orthopedic surgeon to use in clinic, withoutsacrificing the predictive accuracy of the model.

FIG. 20 is a table showing a list of predictive model inputs to machinelearning models for calculating the Global Shoulder Function Score, theVAS Pain Score, and Active Abduction, Active Forward Elevation, andActive External Rotation in accordance with one or more embodiments ofthe present disclosure.

FIG. 21 is a table showing a list of additional predictive model inputsto machine learning models for calculating an ASES score in accordancewith one or more embodiments of the present disclosure. These arepredictive model inputs in addition to what is presented in FIG. 20 .

FIG. 22 is a table showing a list of additional predictive model inputsto machine learning models for calculating a Constant Score inaccordance with one or more embodiments of the present disclosure. Theseare predictive model inputs in addition to what is presented in FIG. 20. The Pre-CT planning predictive model and the Post-CT Predictive modelof FIGS. 20-22 may be equivalent to the initial Pre-Op Prediction MLM 50and the Image-Based Prediction MLM 52 in the system 10 of FIG. 1 ,respectively.

In some embodiments, a three-fold predictive outcomes model, (1. ActiveROM, Pain Scores, and Global Shoulder Function Scores = 19 user inputs,2. ASES = 10 additional user inputs, and 3. Constant = 20 additionaluser inputs) may be formulated, which may be divided into two steps ofgenerating a first predictive model also referred to herein as aninitial preop prediction model using data inputs prior to an image-based(e.g., 3D CT-based) surgical planning step, and generating a secondpredictive model also referred to herein as a final preop predictionmodel which includes additional data taken from the image-based (e.g.,3D CT-based) surgical planning step. The data used in the firstpredictive model may utilize patient demographics, diagnosis,comorbidities, patient history, physician measures of active range ofmotion, patient-specific answers to a few highly-predictive questions,and also patient-specific answers for the questions composing the ASESand Constant scores. A full list of these questions for these 3-foldoutcomes models is demonstrated in the tables of FIGS. 20, 21, and 22 ,respectively.

In some embodiments, the data used in the second predictive model mayutilize outputs from the surgeon directed positioning of the idealimplant size, type, and position to fit the patient’s bony anatomy inthe image-based (e.g., 3D CT) reconstruction surgical planning step. Theproposed workflow describing the flow of the patient from clinic and tosurgery, and how these predictive models pre-medical imaging (pre-CT)planning and post medical imaging (post-CT) planning may be utilized todetermine the appropriate treatment at each stage is described in FIG.23 .

FIG. 23 is an exemplary flow diagram 100 for modeling predictiveoutcomes of arthroplasty surgical procedures in accordance with one ormore embodiments of the present disclosure. The exemplary flow diagram100 with reference to FIG. 1 may include the patient 25 entering aclinic (step 105) to consult with the doctor 20 about an arthroplastysurgical procedure to improve or replace a joint. The doctor 20 maycollect pre-op patient-specific data from the patient 25 that may beentered into the patient-specific data collection module 46 executed bythe processor 45 on the computing device 77. Alternatively, and/oroptionally, the patient-specific data collection module 46 may query theplurality of N electronic resources (40A and 40B) for patient-specificpre-operative data that may be received by the server 15 over thecommunication network 30. The received dataset may include pre-operativepatient specific data for an arthroplasty surgery to be performed on ajoint of a patient where the pre-operative patient specific data mayfurther include a medical history of the patient, a measured range ofmovement for at least one type of joint movement, at least one painmetric associated with the joint, or any combination thereof.

In some embodiments, the received pre-operative patient specific datamay be inputted to an initial preop prediction machine learning model(MLM) 115 (e.g., the initial preop prediction MLM 50 of FIG. 1 ) alsoreferred to herein as a first machine learning model.

In some embodiments, the initial preop prediction MLM 115 may determinea first predicted post-operative joint performance data output thatincludes at least one first predicted post-operative performance metricof the joint, which may then be displayed on the display of thecomputing device 77 to a user, such as the doctor 20, for example.

In some embodiments, the doctor 20 and the patient 25 may have aninitial patient consultation 120. The doctor 20 and/or the patient 25may decide to continue with the arthroplasty surgery of the j oint, orto delay the surgery or to pursue other treatment 125 for the diseasedjoint.

In some embodiments, the doctor 20 may request that the patient 25 mayreceive at least one medical image of the joint, such as a computerizedtomography (CT) scan 130, obtained from at least one medical imagingprocedure performed on the patient 25. The at least one medical image ofthe joint may include an X-ray image, a computerized tomography (CT)image, a magnetic resonance image, a three-dimensional (3D) image,and/or a 3D medical image based on multiple X-ray images. The at leastone medical image of the joint may also include images of the bonesand/or the connective tissues attached to and/or forming the joint.

In some embodiments, in a guided personalized surgery (GPS) PreopPlanning 135 step, the CT image-based (GPS) Joint ReconstructionPlanning module 48, which may be a software program executed by theprocessor 45 on the server 15, may generate a reconstruction plan of thejoint that is display on the GUI 75. The CT image-based (GPS) JointReconstruction Planning module 48 may also be referred to herein as theGPS Planning Software as in FIG. 20 .

In some embodiments, the reconstruction plan may utilize at least onearthroplasty surgical parameter chosen by the doctor in response to thedoctor viewing the first predicted post-operative joint performance dataoutput. The reconstruction plan may include at least one arthroplastysurgical parameter that is selected from, but not limited to, at leastone implant, at least one implant size, at least one arthroplastysurgical procedure, and/or at least one position for implanting the atleast one implant in the joint. The reconstruction plan may includedifferent views of the at least one medical image of the joint, such asthe CT scan 130, that may be displayed on the GUI 75 along with imagesof the at least one implant implanted in the j oint. In otherembodiments, for the case of shoulder arthroplasty, the at least onearthroplasty surgical parameter may also include any of the user inputsfrom the GPS Planning Software as shown in the table of FIG. 20 .

In some embodiments, the at least one arthroplasty surgical parametermay be inputted to a final Preop prediction model 140 (e.g., theimage-based prediction MLM 52 of FIG. 1 ) also referred to herein as asecond machine learning model. The at least one arthroplasty surgicalparameter may include any of the data inputs to the Post-CT PlanningPredictive Model (e.g., final Preop prediction model 140) such as shownin the table of FIG. 20 , for example, for shoulder arthroplasty. Inother embodiments, the data inputs to the second machine learning modelmay include any of the inputs to the first machine learning model aswell as any suitable parameters extracted from the reconstruction plan.In some embodiments, the first machine learning model (e.g., the initialpreop prediction MLM 115) and the second machine learning model (e.g.,final Preop prediction model 140) may be the same machine learningmodel.

In some embodiments, a software application for modeling the predictiveoutcomes of arthroplasty surgical procedures executed by the processor45 may include any or all of the software modules: the patient-specificdata collection module 46, the CT image-based guided personalizedsurgery (GPS) Joint Reconstruction Planning module 48, the initialpre-op prediction machine learning model (MLM) module 50, theimage-based Prediction MLM module 52, the machine learning modeltraining module 54, and/or the GUI manager module 56. In otherembodiments, the initial pre-op prediction machine learning model (MLM)module 50 and the image-based Prediction MLM module 52 may be the samemachine learning model.

In some embodiments, the software application for modeling thepredictive outcomes of arthroplasty surgical procedures may be executedby the processor 45 and the GUI manager 56 may remotely control the GUI75 running on the computing device 77 for providing inputs and/oroutputs from the server 15.

In some embodiments, the first predicted post-operative jointperformance data output and/or the second predicted post-operative jointperformance data output may be displayed on the GUI 75 to the doctor 20in any suitable format, such as outputting a list of predictedpost-operative outcome metrics of the joint based on data inputs such aspre-operative patient specific data, medical images of the joint, andarthroplasty surgical parameters to the predictive outcome machinelearning models. A visual representation of the implant implanted in ajoint based on the medical images of the joint. The visualrepresentation of the implant implanted in a joint may include raw,enhanced, and/or augmented images of the joint that may be displayed onGUI 75.

In some embodiments, the second predicted post-operative jointperformance data output may include displaying on the GUI 75 at leastone arthroplasty surgery recommendation of combinations of surgicalprocedures, implant types, implant sizes, implant positions along withthe predicted post-operative outcome metrics from the models for eachcombination so as to allow the surgeon to optimize the post-operativejoint performance by varying the arthroplasty surgical parameters. Thisoptimization may be performed before and/or during the surgery.

In some embodiments, the at least one arthroplasty surgeryrecommendation may include a recommendation not to proceed with thearthroplasty surgical procedure and/or to pursue another treatment.

In some embodiments, the final Preop prediction model 140 may determinea second predicted post-operative joint performance data output thatincludes the at least one second predicted post-operative performancemetric of the j oint, which may then be displayed on the GUI 75 of thecomputing device 77 to a user, such as the doctor 20, for example.

In some embodiments, the doctor 20 may review second predictedpost-operative joint performance data output and conduct a final patientconsultation 145 with the patient 25. The doctor 20 and/or the patient25 may decide to schedule the arthroplasty surgery 155 of the joint, orto delay the surgery or to pursue other treatment 150 for the diseasedjoint.

FIG. 24 is a table showing a comparison of Mean Absolute Error (MAE)associated with the ASES predictions using the Full and AbbreviatedXGBoost machine learning models in accordance with one or moreembodiments of the present disclosure.

FIG. 25 is a table showing a comparison of Mean Absolute Error (MAE)associated with the constant predictions using the Full and AbbreviatedXGBoost machine learning models in accordance with one or moreembodiments of the present disclosure.

FIG. 26 is a table showing a comparison of Mean Absolute Error (MAE)associated with the Global Shoulder Function Score Predictions using theFull and Abbreviated XGBoost machine learning models in accordance withone or more embodiments of the present disclosure.

FIG. 27 is a table showing a comparison of Mean Absolute Error (MAE)associated with the VAS Pain Score Predictions using the Full andAbbreviated XGBoost machine learning models in accordance with one ormore embodiments of the present disclosure.

FIG. 28 is a table showing a comparison of Mean Absolute Error (MAE)associated with the Active Abduction Predictions using the Full andAbbreviated XGBoost machine learning models in accordance with one ormore embodiments of the present disclosure.

FIG. 29 is a table showing a comparison of Mean Absolute Error (MAE)associated with the Active Forward Elevation Predictions using the Fulland Abbreviated XGBoost machine learning models in accordance with oneor more embodiments of the present disclosure.

FIG. 30 is a table showing a comparison of Mean Absolute Error (MAE)associated with the Active External Rotation Predictions using the Fulland Abbreviated XGBoost machine learning models in accordance with oneor more embodiments of the present disclosure.

FIG. 31 is a table showing a comparison of a full XGBoost modelpredictions for aTSA and rTSA patients that experienced a clinicalimprovement exceeding the MCID threshold for multiple different outcomemeasures in accordance with one or more embodiments of the presentdisclosure.

FIG. 32 is a table showing a comparison of an abbreviated XGBoost modelpredictions for aTSA and rTSA patients that experienced a clinicalimprovement exceeding the MCID threshold for multiple different outcomemeasures in accordance with one or more embodiments of the presentdisclosure.

FIG. 33 is a table showing a comparison of a full XGBoost modelpredictions for aTSA and rTSA patients that experienced a clinicalimprovement exceeding the SCB threshold for multiple different outcomemeasures in accordance with one or more embodiments of the presentdisclosure.

FIG. 34 is a table showing a comparison of an abbreviated XGBoost modelpredictions for aTSA and rTSA patients that experienced a clinicalimprovement exceeding the SCB threshold for multiple different outcomemeasures in accordance with one or more embodiments of the presentdisclosure.

FIG. 35 is a table showing a comparison of an abbreviated XGBoost modelwith inputs from CT planning data to make predictions for aTSA and rTSApatients that experienced a clinical improvement exceeding the MCIDthreshold for multiple different outcome measures in accordance with oneor more embodiments of the present disclosure.

FIG. 36 is a table showing a comparison of an abbreviated XGBoost modelwith inputs from CT planning data to make predictions for aTSA and rTSApatients that experienced a clinical improvement exceeding the SCBthreshold for multiple different outcome measures in accordance with oneor more embodiments of the present disclosure.

In some embodiments, the model inputs (in the pre-planning andpost-planning phases of the predictive models) may be the mosthighly-predictive parameters, which may provide very similar levels ofpredictive accuracy similar to the case of using all variables in theclinical outcome database 62. As demonstrated in the tables of FIGS.24-30 , the results of the abbreviated model may yield nearly identicalaccuracy for each outcome metric as the predictive model which datainputs from the entire the clinical outcome database 62.

In some embodiments, the prediction accuracy between aTSA and rTSA wereobserved to be similar, for both the full and abbreviated models.Additionally, for both the full and abbreviated prediction models, MAEwas found to be slightly higher at earlier post-operative timepointsthan at later post-operative timepoints. Across all post-operativetimepoints analyzed, the average difference in MAE between the full andabbreviated model predications was found to be ±0.3 MAE for the ASESscore (±0.3 aTSA and ±0.4 rTSA), ±0.9 for the Constant score (±0.7 aTSAand ±0.8 rTSA), ±0.1 for the Global Shoulder Function score (±0.1 aTSAand ±0.1 rTSA), ±0.1 for the VAS pain score (±0.0 aTSA and ±0.2 rTSA),±1.4° for abduction (±1.1 aTSA and ±1.2 rTSA), ±1.6° for forwardelevation (±1.7 aTSA and ±1.4 rTSA), and ±0.4° for external rotation(±0.1 aTSA and ±0.4 rTSA).

In some embodiments, as demonstrated in the tables of FIGS. 31-34 , theabbreviated models yielded nearly-identical MCID and SCB accuracyresults as well, demonstrating the ability of these models toeffectively risk-stratify patients prior to surgery based upon theirability to achieve varying magnitudes of improvement at 2-3 years offollow-up according to multiple different outcome metrics.

In some embodiments, specifically regarding the MCID, the fullpredictive models achieved 82-96% accuracy in MCID with an AUROC between0.75-0.97 for aTSA patients; whereas, the abbreviated predictive modelsachieved 82-96% accuracy in MCID with an AUROC between 0.70-0.95 foraTSA patients. The full predictive models achieved 91-99% accuracy inMCID with an AUROC between 0.82-0.98 for rTSA patients; whereas, theabbreviated predictive models achieved 91-99% accuracy in MCID with anAUROC between 0.84-0.94 for rTSA patients.

In some embodiments, similarly regarding the SCB, the full predictivemodels achieved 79-90% accuracy in SCB with an AUROC between 0.74-0.90for aTSA patients; whereas, the abbreviated predictive models achieved76-90% accuracy in SCB with an AUROC between 0.70-0.89 for aTSApatients. Finally, the full predictive models achieved 83-92% accuracyin SCB with an AUROC between 0.78-0.88 for rTSA patients; whereas, theabbreviated predictive models achieved 81-90% accuracy in SCB with anAUROC between 0.70-0.87 for rTSA patients. With regard to theinterpretation of AUROC values used in these MCID and SCB predictions,0.5 is considered random, >0.7 is considered acceptable, >0.8 isconsidered good, and >0.9 is considered excellent discrimination for apredictive model.

In some embodiments, for the abbreviated model algorithms, the averageMCID AUROC values were 0.82 for aTSA and 0.89 for rTSA and the averageSCB AUROC values were 0.85 for aTSA and 0.82 for rTSA, suggesting thesealgorithms generated from a minimal feature set exhibit on average,between good and excellent discrimination, and at worst, acceptablediscrimination. These abbreviated model prediction values may beimproved by adding in the selected implant data from the guidedpersonalized surgery (GPS) CT planning, as demonstrated in the tables ofFIGS. 24-30 and 35-36 . Note that other predictive models may begenerated using this data and the techniques disclosed herein, such asthe internal rotation score, visual analogue pain at worst, and also theshoulder arthroplasty smart score.

FIG. 24 is a table showing a comparison of Mean Absolute Error (MAE)associated with the ASES predictions using the Full and AbbreviatedXGBoost machine learning models in accordance with one or moreembodiments of the present disclosure.

FIG. 25 is a table showing a comparison of Mean Absolute Error (MAE)associated with the constant predictions using the Full and AbbreviatedXGBoost machine learning models in accordance with one or moreembodiments of the present disclosure.

FIG. 26 is a table showing a comparison of Mean Absolute Error (MAE)associated with the Global Shoulder Function Score Predictions using theFull and Abbreviated XGBoost machine learning models in accordance withone or more embodiments of the present disclosure.

FIG. 27 is a table showing a comparison of Mean Absolute Error (MAE)associated with the VAS Pain Score Predictions using the Full andAbbreviated XGBoost machine learning models in accordance with one ormore embodiments of the present disclosure.

FIG. 28 is a table showing a comparison of Mean Absolute Error (MAE)associated with the Active Abduction Predictions using the Full andAbbreviated XGBoost machine learning models in accordance with one ormore embodiments of the present disclosure.

FIG. 29 is a table showing a comparison of Mean Absolute Error (MAE)associated with the Active Forward Elevation Predictions using the Fulland Abbreviated XGBoost machine learning models in accordance with oneor more embodiments of the present disclosure.

FIG. 30 is a table showing a comparison of Mean Absolute Error (MAE)associated with the Active External Rotation Predictions using the Fulland Abbreviated XGBoost machine learning models in accordance with oneor more embodiments of the present disclosure.

Thus, the machine learning predictive models described herein mayeffectively provide the same predictive accuracy of for clinicaloutcomes for aTSA and rTSA, for a given patient prior to arthroplastysurgery based on using more than 75% less user inputs for theabbreviated prediction model than the full prediction model. This largereduction in the user input data enables the use of such a tool in asurgeon’s clinic, as it requires a similar burden of inputs as othercommonly used patient reported outcome metrics to quantify clinicalresults after aTSA and rTSA.

In some embodiments, the machine learning models used in the softwareapplication may be abbreviated machine learning models so as to improvethe computation efficiency and/or to enhance the computing speed of theserver 15 as demonstrated in the tables of the previous figures.

Stated differently, the initial preop prediction MLM 50 and theimage-based prediction MLM 52 may be abbreviated machine learning modelsthat may be referred to respectively herein as the first abbreviated MLMand the second abbreviated MLM

In some embodiments, in addition to the outcome metrics and range ofmotion predictions, the predictive outcome models may identify thefactors that are driving the prediction up and down. Specifically, forthose factors which are modifiable by the patient, the predictiveoutcome models may provide recommendations to the patient on what theycan do to improve the outcomes prediction in order to make the patient amore active participant in the surgeon-patient consultation.

In some embodiments, the predictive outcome models may incorporate alook-up table of typical complication rates that may be associated withaTSA and rTSA for a given patients demographics, diagnosis, patienthistory, and/or comorbidities.

In some embodiments, the predictive outcome models may provideadditional features to the surgeon which may assist in achieving betterpredicted outcomes. For example, if the case was navigated, the outcomescould be improved by 2%, or as another illustrative example, if apatient has 10 degrees of glenoid retroversion, a better outcome may bepredicted using an augmented glenoid component for aTSA and/or rTSA asopposed to a standard component (with or without eccentric glenoidreaming surgical techniques).

In some embodiments, trade-offs between implant technique may beimplemented in order to help the surgeon user improve their decisionmaking. For example, to inform surgeons when to use aTSA versus rTSA forpatients with different rotator cuff tear sizes, to inform surgeons whento use aTSA versus rTSA for different Goutallier rotator cuff fattyinfiltration grades, to inform surgeons when to use bone graft versusaugmented glenoid components for different glenoid deformityclassification types (such as the Walch, Favard, or Antuna) or for aparticular glenoid wear measurement (like retroversion, inclination, orbeta angle), to inform when to perform eccentric glenoid reaming versusoff-axis reaming to correct glenoid wear, and also by how much, and/orto inform when to use a standard length humeral stem versus a shorthumeral stem versus a stemless humeral implant, and what size of eachimplant to select based upon bone quality.

In some embodiments, these arthroplasty surgical parameters may bevaried on-the-fly to allow the surgeon either before surgery or duringsurgery to observe these tradeoffs on the software platform in thesecond predicted post-operative joint performance data output, inresponse to the surgeon (e.g., the user) varying any of the at least onearthroplasty surgical parameter in the reconstruction plan before thearthroplasty surgery, during the arthroplasty surgery, or both.

In some embodiments, data from early post-surgical follow-up visits,such as 2 weeks, 6 weeks, 8 weeks, 12 weeks, 4 months, or earlier may beused to predict outcomes at different post-surgical timepoints. Thebenefit of these post-surgical predictions is that they may potentiallyprovide a more accurate estimation of the patient-specific improvement.The data may be a useful aid in establishing more patient-specificrehabilitation protocols targeting improvement in a given metricrelative to other outcome metrics.

In some embodiments, this data or additional data (and/or incorporateadditional data directly from the patient’s electronic medical record orsome other database, such as data stored in the cloud and/or generatedfrom wearable device which may measure a patient’s movement and/oractivity level, may accept responses from patient related to painlevels, etc.) may be used to further refine these predictive models andcreate more accurate inputs using additional data. The data may alsofurther help risk-stratify patients for shoulder arthroplasty and makerecommendations on healthcare workflows, such as identifying patientswho may safely have surgery in an ambulatory surgical center. Thepredictive models may make recommendations regarding whether a specificpatient should have in-patient vs. outpatient surgery in a hospital.Additionally, the predictive models may also provide recommendations fora specific patient on their duration length for hospital stay after theprocedure.

Finally, as more clinical data is added to the clinical outcome database62 over time, the model training module 54 may be used to update themachine learning algorithms accordingly in order to reduce predictiveerror. Thus, this enables the predictive outcome algorithms tocontinuously learn based upon the input of new data using the tool.Additionally, new parameters may be added in the future and the rank ofthe existing parameters may be changed to further improve the predictivemodels from data directly form CT and/or MRI images, for example, bonedensity, bone architecture, soft tissue tears, and/or other soft tissuedamage, such as rotator cuff fatty infiltration, which may furtherassist the doctor in clinical decision making for treatment and/oroutcomes predictions.

In some embodiments, from these images, glenohumeral or other jointbone-to-bone relationships may be assessed, and the patient specificdata may influence the predictive models as a new input that furtherassists in clinical decision making for treatment or outcomepredictions. With new data, the predictive models may also be moretransferrable and generalizable to other total shoulder arthroplastysystems and perhaps even to other arthroplasty systems for differentjoints and applications such as spine, hip, knee, ankle, trauma, etc).When the predictive outcome models have a greater accuracy ofprediction, better clinical decision making related to the implant type,size, and location may be made and these will result in improvedpatients and surgeon satisfaction with more realistic expectations ofoutcomes.

FIG. 37 is a flowchart of an exemplary method 200 for modelingpredictive outcomes of arthroplasty surgical procedures in accordancewith one or more embodiments of the present disclosure. The method maybe performed by the processor 45 of the server 15.

The method 200 may include receiving 210 pre-operative patient specificdata for an arthroplasty surgery to be performed on a joint of apatient.

The method 200 may include inputting 220 the pre-operative patientspecific data to at least one first machine learning model to determinea first predicted post-operative joint performance data output, wherethe first predicted post-operative joint performance data outputincludes at least one first predicted post-operative outcome metric ofthe joint.

The method 200 may include displaying 230 the first predictedpost-operative joint performance data output on a display to a user.

The method 200 may include receiving 240 at least one medical image ofthe joint obtained from at least one medical imaging procedure performedon the patient.

The method 200 may include generating 250 a reconstruction plan of thejoint of the patient based on the at least one medical image of thejoint, and at least one arthroplasty surgical parameter obtained fromthe user in response to the displayed first predicted post-operativejoint performance data output where the reconstruction plan includes atleast one arthroplasty surgical parameter that is selected from at leastone implant, at least one implant size, at least one arthroplastysurgical procedure, at least one position for implanting the at leastone implant in the joint, or any combination thereof.

The method 200 may include inputting 260 the at least one arthroplastysurgical parameter into at least one second machine learning model todetermine a second predicted post-operative joint performance dataoutput including at least one second predicted post-operative outcomemetric of the joint.

The method 200 may include displaying 270 the second predictedpost-operative joint performance data output on the display to the user.

The method 200 may include updating 280 the displayed second predictedpost-operative joint performance data output to include at least onearthroplasty surgery recommendation, in response to the user varying anyof the at least one arthroplasty surgical parameter, before thearthroplasty surgery, during the arthroplasty surgery, or both. This mayallow the surgeon 20 to adjust any of the surgical parameters foroptimizing any of the predicted post-operative outcome metricson-the-fly either before surgery and/or during the arthroplasty surgicalprocedure.

In some embodiments, an apparatus may include a processor and anon-transitory memory storing instructions which, when executed by theprocessor, cause the processor to:

-   receive pre-operative patient specific data for an arthroplasty    surgery to be performed on a joint of a patient;-   input the pre-operative patient specific data to at least one    machine learning model to determine a first predicted post-operative    joint performance data output;    -   where the first predicted post-operative joint performance data        output may include at least one first predicted post-operative        outcome metric of the joint;-   display the first predicted post-operative joint performance data    output on a display to a user;-   receive at least one medical image of the joint obtained from at    least one medical imaging procedure performed on the patient;-   generate a reconstruction plan of the joint of the patient based on    the at least one medical image of the joint, and at least one    arthroplasty surgical parameter obtained from the user in response    to the displayed first predicted post-operative joint performance    data output;-   input the at least one arthroplasty surgical parameter into the at    least one machine learning model to determine a second predicted    post-operative joint performance data output including at least one    second predicted post-operative outcome metric of the j oint; and-   display the second predicted post-operative joint performance data    output on the display to the user.

In some embodiments, an apparatus may include a processor and anon-transitory memory storing instructions which, when executed by theprocessor, cause the processor to:

-   receive pre-operative patient specific data for an arthroplasty    surgery to be performed on a joint of a patient; where the    pre-operative patient specific data may include:    -   (i) a medical history of the patient,    -   (ii) a measured range of movement for at least one type of joint        movement of the joint, and    -   (iii) at least one pain metric associated with the joint;-   input the pre-operative patient specific data to at least one first    machine learning model to determine a first predicted post-operative    joint performance data output;    -   where the first predicted post-operative joint performance data        output may include at least one first predicted post-operative        outcome metric of the joint;-   display the first predicted post-operative joint performance data    output on a display to a user;-   receive at least one medical image of the joint obtained from at    least one medical imaging procedure performed on the patient;-   generate a reconstruction plan of the joint of the patient based on    the at least one medical image of the joint, and at least one    arthroplasty surgical parameter obtained from the user in response    to the displayed first predicted post-operative joint performance    data output; where the reconstruction plan may include the at least    one arthroplasty surgical parameter that is selected from:    -   (i) at least one implant,    -   (ii) at least one implant size,    -   (iii) at least one arthroplasty surgical procedure,    -   (iv) at least one position for implanting the at least one        implant in the joint, or    -   (v) any combination thereof;-   input the at least one arthroplasty surgical parameter into at least    one second machine learning model to determine a second predicted    post-operative joint performance data output including at least one    second predicted post-operative outcome metric of the joint;-   display the second predicted post-operative joint performance data    output on the display to the user; and-   update the displayed second predicted post-operative joint    performance data output to include at least one arthroplasty surgery    recommendation, in response to the user varying any of the at least    one arthroplasty surgical parameter, before the arthroplasty    surgery, during the arthroplasty surgery, or both.

In some embodiments, the processor may be configured to receive thepre-operative patient specific data by receiving the pre-operativepatient specific data over a communication network from at least oneelectronic medical resource.

In some embodiments, the at least one medical image may include at leastone of: (a) an X-ray image, (b) a computerized tomography image, (c) amagnetic resonance image, (d) a three-dimensional (3D) image, (e) a 3Dmedical image generated from multiple X-ray images, (f) a frame of avideo, or any combination thereof.

In some embodiments, the at least one first predicted post-operativeoutcome metric and at least one second predicted post-operative outcomemetric may be predicted for at least one of: (a) a number of days, (b) anumber of months, and (c) a number of years.

In some embodiments, the processor may be configured to display thesecond predicted post-operative joint performance data output withrecommendations for the at least one arthroplasty surgical parameter.

In some embodiments, the joint may be selected from the group consistingof a hip joint, a knee j oint, a shoulder joint, an elbow joint, and anankle joint.

In some embodiments, the joint may be a shoulder joint.

In some embodiments, the pre-operative patient specific data mayinclude: (a) patient demographics, (b) a patient diagnosis, (c) apatient comorbidity, (d) a patient medical history, (e) a shoulderactive range of motion measure, (f) a patient self-reported measure ofpain, function, or both, (g) a patient score based on American Shoulderand Elbow Surgeons Shoulder Score (ASES), (h) a patient score based onConstant Shoulder Score (CSS), or any combination thereof.

In some embodiments, the at least one arthroplasty surgical proceduremay be selected from the group consisting of an anatomic total shoulderarthroplasty, a reverse total shoulder arthroplasty, deltopectoraltechnique, and a superior-lateral technique.

In some embodiments, the at least one first predicted post-operativeoutcome metric and the at least one second predicted post-operativeoutcome metric may be selected from the group consisting of an AmericanShoulder and Elbow (ASES) score, a University of California, Los Angeles(UCLA) score, a constant score, a global shoulder function score, aVisual Analogue Scale (VAS) Pain score, a smart shoulder arthroplastyscore, an internal rotation (IR) score, an abduction measurement, aforward elevation measurement, and an external rotation measurement.

In some embodiments, a method may include:

-   receiving, by a processor, pre-operative patient specific data for    an arthroplasty surgery to be performed on a joint of a patient;-   inputting, by the processor, the pre-operative patient specific data    to at least one machine learning model to determine a first    predicted post-operative joint performance data output;    -   where the first predicted post-operative joint performance data        output may include at least one first predicted post-operative        outcome metric of the joint;-   displaying, by the processor, the first predicted post-operative    joint performance data output on a display to a user;-   receiving, by the processor, at least one medical image of the joint    obtained from at least one medical imaging procedure performed on    the patient;-   generating, by the processor, a reconstruction plan of the joint of    the patient based on the at least one medical image of the joint,    and at least one arthroplasty surgical parameter obtained from the    user in response to the displayed first predicted post-operative    joint performance data output;-   inputting, by the processor, the reconstruction plan into the at    least one machine learning model to determine a second predicted    post-operative joint performance data output including at least one    second predicted post-operative outcome metric of the joint; and-   displaying, by the processor, the second predicted post-operative    joint performance data output on the display to the user.

In some embodiments, a method may include:

-   receiving, by a processor, pre-operative patient specific data for    an arthroplasty surgery to be performed on a joint of a patient;    where the pre-operative patient specific data includes:    -   (i) a medical history of the patient,    -   (ii) a measured range of movement for at least one type of joint        movement of the joint, and    -   (iii) at least one pain metric associated with the joint;-   inputting, by the processor, the pre-operative patient specific data    to at least one first machine learning model to determine a first    predicted post-operative joint performance data output;    -   where the first predicted post-operative joint performance data        output may include at least one first predicted post-operative        outcome metric of the joint;-   displaying, by the processor, the first predicted post-operative    joint performance data output on a display to a user;-   receiving, by the processor, at least one medical image of the joint    obtained from at least one medical imaging procedure performed on    the patient;-   generating, by the processor, a reconstruction plan of the joint of    the patient based on the at least one medical image of the joint,    and at least one arthroplasty surgical parameter obtained from the    user in response to the displayed first predicted post-operative    joint performance data output; where the reconstruction plan may    include the at least one arthroplasty surgical parameter that is    selected from:    -   (i) at least one implant,    -   (ii) at least one implant size,    -   (iii) at least one arthroplasty surgical procedure,    -   (iv) at least one position for implanting the at least one        implant in the joint, or    -   (v) any combination thereof;-   inputting, by the processor, the reconstruction plan into at least    one second machine learning model to determine a second predicted    post-operative joint performance data output including at least one    second predicted post-operative outcome metric of the joint;-   displaying, by the processor, the second predicted post-operative    joint performance data output on the display to the user; and-   updating, by the processor, the displayed second predicted    post-operative joint performance data output to include at least one    arthroplasty surgery recommendation, in response to the user varying    any of the at least one arthroplasty surgical parameter in the    reconstruction plan, before the arthroplasty surgery, during the    arthroplasty surgery, or both.

In some embodiments, receiving the pre-operative patient specific datamay include receiving the pre-operative patient specific data over acommunication network from at least one electronic medical resource.

In some embodiments, the at least one medical image may include at leastone of: (a) an X-ray image, (b) a computerized tomography image, (c) amagnetic resonance image, (d) a three-dimensional (3D) image, (e) a 3Dmedical image generated from multiple X-ray images, (f) a frame of avideo, or any combination thereof.

In some embodiments, the at least one first predicted post-operativeoutcome metric and at least one second predicted post-operative outcomemetric may be predicted for at least one of: (a) a number of days, (b) anumber of months, and (c) a number of years.

In some embodiments, displaying the second predicted post-operativejoint performance data output may include displaying the secondpredicted post-operative joint performance data output withrecommendations for the at least one arthroplasty surgical parameter.

In some embodiments, the joint may be selected from the group consistingof a hip joint, a knee joint, a shoulder joint, an elbow joint, and anankle joint.

In some embodiments, the joint may be a shoulder joint.

In some embodiments, the pre-operative patient specific data mayinclude: (a) patient demographics, (b) a patient diagnosis, (c) apatient comorbidity, (d) a patient medical history, (e) a shoulderactive range of motion measure, (f) a patient self-reported measure ofpain, function, or both, (g) a patient score based on American Shoulderand Elbow Surgeons Shoulder Score (ASES), (h) a patient score based onConstant Shoulder Score (CSS), a shoulder arthroplasty smart score, orany combination thereof.

In some embodiments, the at least one arthroplasty surgical proceduremay be selected from the group consisting of an anatomic total shoulderarthroplasty, a reverse total shoulder arthroplasty, deltopectoraltechnique, and a superior-lateral technique.

In some embodiments, the at least one first predicted post-operativeoutcome metric and the at least one second predicted post-operativeoutcome metric may be selected from the group consisting of an AmericanShoulder and Elbow (ASES) score, a University of California, Los Angeles(UCLA) score, a constant score, a global shoulder function score, aVisual Analogue Scale (VAS) Pain score, a smart shoulder arthroplastyscore, an internal rotation (IR) score, an abduction measurement, aforward elevation measurement, and an external rotation measurement.

In some embodiments, exemplary inventive, specially programmed computingsystems/platforms with associated devices are configured to operate inthe distributed network environment, communicating with one another overone or more suitable data communication networks (e.g., the Internet,satellite, etc.) and utilizing one or more suitable data communicationprotocols/modes such as, without limitation, IPX/SPX, X.25, AX.25,AppleTalk(TM), TCP/IP (e.g., HTTP), near-field wireless communication(NFC), RFID, Narrow Band Internet of Things (NBIOT), 3G, 4G, 5G, GSM,GPRS, WiFi, WiMax, CDMA, satellite, ZigBee, and other suitablecommunication modes. In some embodiments, the NFC can represent ashort-range wireless communications technology in which NFC-enableddevices are “swiped,” “bumped,” “tap” or otherwise moved in closeproximity to communicate. In some embodiments, the NFC could include aset of short-range wireless technologies, typically requiring a distanceof 10 cm or less. In some embodiments, the NFC may operate at 13.56 MHzon ISO/IEC 18000-3 air interface and at rates ranging from 106 kbit/s to424 kbit/s. In some embodiments, the NFC can involve an initiator and atarget; the initiator actively generates an RF field that can power apassive target. In some embodiments, this can enable NFC targets to takevery simple form factors such as tags, stickers, key fobs, or cards thatdo not require batteries. In some embodiments, the NFC’s peer-to-peercommunication can be conducted when a plurality of NFC-enable devices(e.g., smartphones) within close proximity of each other.

The material disclosed herein may be implemented in software or firmwareor a combination of them or as instructions stored on a machine-readablemedium, which may be read and executed by one or more processors. Amachine-readable medium may include any medium and/or mechanism forstoring or transmitting information in a form readable by a machine(e.g., a computing device). For example, a machine-readable medium mayinclude read only memory (ROM); random access memory (RAM); magneticdisk storage media; optical storage media; flash memory devices;electrical, optical, acoustical or other forms of propagated signals(e.g., carrier waves, infrared signals, digital signals, etc.), andothers.

Examples of hardware elements may include processors, microprocessors,circuits, circuit elements (e.g., transistors, resistors, capacitors,inductors, and so forth), integrated circuits, application specificintegrated circuits (ASIC), programmable logic devices (PLD), digitalsignal processors (DSP), field programmable gate array (FPGA), logicgates, registers, semiconductor device, chips, microchips, chip sets,and so forth. In some embodiments, the one or more processors may beimplemented as a Complex Instruction Set Computer (CISC) or ReducedInstruction Set Computer (RISC) processors; x86 instruction setcompatible processors, multi-core, or any other microprocessor orcentral processing unit (CPU). In various implementations, the one ormore processors may be dual-core processor(s), dual-core mobileprocessor(s), and so forth.

Computer-related systems, computer systems, and systems, as used herein,include any combination of hardware and software. Examples of softwaremay include software components, operating system software, middleware,firmware, software modules, routines, subroutines, functions, methods,procedures, software interfaces, application program interfaces (API),instruction sets, computer code, computer code segments, words, values,symbols, or any combination thereof. Determining whether an embodimentis implemented using hardware elements and/or software elements may varyin accordance with any number of factors, such as desired computationalrate, power levels, heat tolerances, processing cycle budget, input datarates, output data rates, memory resources, data bus speeds and otherdesign or performance constraints.

One or more aspects of at least one embodiment may be implemented byrepresentative instructions stored on a machine-readable medium whichrepresents various logic within the processor, which when read by amachine causes the machine to fabricate logic to perform the techniquesdescribed herein. Such representations, known as “IP cores” may bestored on a tangible, machine readable medium and supplied to variouscustomers or manufacturing facilities to load into the fabricationmachines that make the logic or processor. Of note, various embodimentsdescribed herein may, of course, be implemented using any appropriatehardware and/or computing software languages (e.g., C++, Objective-C,Swift, Java, JavaScript, Python, Perl, QT, etc.).

In some embodiments, one or more of exemplary inventive computer-basedsystems/platforms, exemplary inventive computer-based devices, and/orexemplary inventive computer-based components of the present disclosuresuch as the computing device 77 may include or be incorporated,partially or entirely into at least one personal computer (PC), laptopcomputer, ultra-laptop computer, tablet, touch pad, portable computer,handheld computer, palmtop computer, personal digital assistant (PDA),cellular telephone, combination cellular telephone/PDA, television,smart device (e.g., smart phone, smart tablet or smart television),mobile internet device (MID), messaging device, data communicationdevice, and so forth.

As used herein, the term “server” should be understood to refer to aservice point which provides processing, database, and communicationfacilities. By way of example, and not limitation, the term “server” canrefer to a single, physical processor with associated communications anddata storage and database facilities, or it can refer to a networked orclustered complex of processors and associated network and storagedevices, as well as operating software and one or more database systemsand application software that support the services provided by theserver. Cloud servers are examples.

In some embodiments, as detailed herein, one or more of exemplaryinventive computer-based systems/platforms, exemplary inventivecomputer-based devices, and/or exemplary inventive computer-basedcomponents of the present disclosure may obtain, manipulate, transfer,store, transform, generate, and/or output any digital object and/or dataunit (e.g., from inside and/or outside of a particular application) thatcan be in any suitable form such as, without limitation, a file, acontact, a task, an email, a social media post, a map, an entireapplication (e.g., a calculator), etc. In some embodiments, as detailedherein, one or more of exemplary inventive computer-basedsystems/platforms, exemplary inventive computer-based devices, and/orexemplary inventive computer-based components of the present disclosuremay be implemented across one or more of various computer platforms suchas, but not limited to: (1) FreeBSD, NetBSD, OpenBSD; (2) Linux; (3)Microsoft Windows; (4) OS X (MacOS); (5) MacOS 11; (6) Solaris; (7)Android; (8) iOS; (9) Embedded Linux; (10) Tizen; (11) WebOS; (12) IBMi; (13) IBM AIX; (14) Binary Runtime Environment for Wireless (BREW);(15) Cocoa (API); (16) Cocoa Touch; (17) Java Platforms; (18) JavaFX;(19) JavaFX Mobile; (20) Microsoft DirectX; (21) .NET Framework; (22)Silverlight; (23) Open Web Platform; (24) Oracle Database; (25) Qt; (26)Eclipse Rich Client Platform; (27) SAP NetWeaver; (28) Smartface; and/or(29) Windows Runtime.

In some embodiments, exemplary inventive computer-basedsystems/platforms, exemplary inventive computer-based devices, and/orexemplary inventive computer-based components of the present disclosuremay be configured to utilize hardwired circuitry that may be used inplace of or in combination with software instructions to implementfeatures consistent with principles of the disclosure. Thus,implementations consistent with principles of the disclosure are notlimited to any specific combination of hardware circuitry and software.For example, various embodiments may be embodied in many different waysas a software component such as, without limitation, a stand-alonesoftware package, a combination of software packages, or it may be asoftware package incorporated as a “tool” in a larger software product.

For example, exemplary software specifically programmed in accordancewith one or more principles of the present disclosure may bedownloadable from a network, for example, a website, as a stand-aloneproduct or as an add-in package for installation in an existing softwareapplication. For example, exemplary software specifically programmed inaccordance with one or more principles of the present disclosure mayalso be available as a client-server software application, or as aweb-enabled software application. For example, exemplary softwarespecifically programmed in accordance with one or more principles of thepresent disclosure may also be embodied as a software package installedon a hardware device.

In some embodiments, exemplary inventive computer-basedsystems/platforms, exemplary inventive computer-based devices, and/orexemplary inventive computer-based components of the present disclosuremay be configured to handle numerous concurrent users that may be, butis not limited to, at least 100 (e.g., but not limited to, 100-999), atleast 1,000 (e.g., but not limited to, 1,000-9,999 ), at least 10,000(e.g., but not limited to, 10,000-99,999 ), at least 100,000 (e.g., butnot limited to, 100,000-999,999), at least 1,000,000 (e.g., but notlimited to, 1,000,000-9,999,999), at least 10,000,000 (e.g., but notlimited to, 10,000,000-99,999,999), at least 100,000,000 (e.g., but notlimited to, 100,000,000-999,999,999), at least 1,000,000,000 (e.g., butnot limited to, 1,000,000,000-999,999,999,999), and so on.

In some embodiments, exemplary inventive computer-basedsystems/platforms, exemplary inventive computer-based devices, and/orexemplary inventive computer-based components of the present disclosuremay be configured to output to distinct, specifically programmedgraphical user interface implementations of the present disclosure(e.g., a desktop, a web app., etc.). In various implementations of thepresent disclosure, a final output may be displayed on a displayingscreen which may be, without limitation, a screen of a computer, ascreen of a mobile device, or the like. In various implementations, thedisplay may be a holographic display. In various implementations, thedisplay may be a transparent surface that may receive a visualprojection. Such projections may convey various forms of information,images, and/or objects. For example, such projections may be a visualoverlay for a mobile augmented reality (MAR) application.

As used herein, the term “mobile electronic device,” or the like, mayrefer to any portable electronic device that may or may not be enabledwith location tracking functionality (e.g., MAC address, InternetProtocol (IP) address, or the like). For example, a mobile electronicdevice can include, but is not limited to, a mobile phone, PersonalDigital Assistant (PDA), Blackberry ™, Pager, Smartphone, or any otherreasonable mobile electronic device.

As used herein, the terms “cloud,” “Internet cloud,” “cloud computing,”“cloud architecture,” and similar terms correspond to at least one ofthe following: (1) a large number of computers connected through areal-time communication network (e.g., Internet); (2) providing theability to run a program or application on many connected computers(e.g., physical machines, virtual machines (VMs)) at the same time; (3)network-based services, which appear to be provided by real serverhardware, and are in fact served up by virtual hardware (e.g., virtualservers), simulated by software running on one or more real machines(e.g., allowing to be moved around and scaled up (or down) on the flywithout affecting the end user).

In some embodiments, the exemplary inventive computer-basedsystems/platforms, the exemplary inventive computer-based devices,and/or the exemplary inventive computer-based components of the presentdisclosure may be configured to securely store and/or transmit data byutilizing one or more of encryption techniques (e.g., private/public keypair, Triple Data Encryption Standard (3DES), block cipher algorithms(e.g., IDEA, RC2, RC5, CAST and Skipjack), cryptographic hash algorithms(e.g., MD5, RIPEMD-160, RTR0, SHA-1, SHA-2, Tiger (TTH),WHIRLPOOL,RNGs).

The aforementioned examples are, of course, illustrative and notrestrictive.

As used herein, the term “user” shall have a meaning of at least oneuser. In the context as used herein, the user may be a doctor, or asurgeon or someone acting on behalf of the doctor, or surgeon, alaboratory technician, surgical staff, and the like.

In some embodiments, the exemplary inventive computer-basedsystems/platforms, the exemplary inventive computer-based devices,and/or the exemplary inventive computer-based components of the presentdisclosure may be configured to utilize one or more exemplary AI/machinelearning techniques chosen from, but not limited to, decision trees,boosting, support-vector machines, neural networks, nearest neighboralgorithms, Naive Bayes, bagging, random forests, and the like. In someembodiments and, optionally, in combination of any embodiment describedabove or below, an exemplary neutral network technique may be one of,without limitation, feedforward neural network, radial basis functionnetwork, recurrent neural network, convolutional network (e.g., U-net)or other suitable network. In some embodiments and, optionally, incombination of any embodiment described above or below, an exemplaryimplementation of Neural Network may be executed as follows:

-   i) Define Neural Network architecture/model,-   ii) Transfer the input data to the exemplary neural network model,-   iii) Train the exemplary model incrementally,-   iv) determine the accuracy for a specific number of timesteps,-   v) apply the exemplary trained model to process the newly-received    input data,-   vi) optionally and in parallel, continue to train the exemplary    trained model with a predetermined periodicity.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the exemplary trained neural network model mayspecify a neural network by at least a neural network topology, a seriesof activation functions, and connection weights. For example, thetopology of a neural network may include a configuration of nodes of theneural network and connections between such nodes. In some embodimentsand, optionally, in combination of any embodiment described above orbelow, the exemplary trained neural network model may also be specifiedto include other parameters, including but not limited to, biasvalues/functions and/or aggregation functions. For example, anactivation function of a node may be a step function, sine function,continuous or piecewise linear function, sigmoid function, hyperbolictangent function, or other type of mathematical function that representsa threshold at which the node is activated. In some embodiments and,optionally, in combination of any embodiment described above or below,the exemplary aggregation function may be a mathematical function thatcombines (e.g., sum, product, etc.) input signals to the node. In someembodiments and, optionally, in combination of any embodiment describedabove or below, an output of the exemplary aggregation function may beused as input to the exemplary activation function. In some embodimentsand, optionally, in combination of any embodiment described above orbelow, the bias may be a constant value or function that may be used bythe aggregation function and/or the activation function to make the nodemore or less likely to be activated.

The disclosure described herein may be practiced in the absence of anyelement or elements, limitation or limitations, which is notspecifically disclosed herein. Thus, for example, in each instanceherein, any of the terms “comprising,” “consisting essentially of and“consisting of” may be replaced with either of the other two terms,without altering their respective meanings as defined herein. The termsand expressions which have been employed are used as terms ofdescription and not of limitation, and there is no intention in the useof such terms and expressions of excluding any equivalents of thefeatures shown and described or portions thereof, but it is recognizedthat various modifications are possible within the scope of thedisclosure.

1-22. (canceled)
 23. A system, comprising: a non-transitory memorystoring software instructions; at least one processor that, whenexecuting the software instructions, is configured to: receivepre-operative patient specific data for an arthroplasty surgery to beperformed on a joint of a patient; wherein the pre-operative patientspecific data comprises: (i) a medical history of the patient, (ii) ameasured range of movement for at least one type of joint movement ofthe j oint, and (iii) at least one pain metric associated with thejoint; receive at least one medical image of the joint obtained from atleast one medical imaging procedure performed on the patient; receive atleast one arthroplasty surgical parameter; wherein the at least onearthroplasty surgical parameter is selected from: (i) at least oneimplant, (ii) at least one implant size, (iii) at least one arthroplastysurgical procedure, (iv) at least one position for implanting the atleast one implant in the j oint, or (v) any combination thereof;generate a reconstruction plan of the joint of the patient based atleast in part on the at least one medical image of the joint and the atleast one arthroplasty surgical parameter; input the pre-operativepatient specific data and reconstruction plan data into at least onemachine learning model to determine a predicted post-operative jointperformance data output at a plurality of post-operative timepointsafter surgery; wherein the at least one machine learning model istrained to output data comprising a plurality of values for thepredicted post-operative joint performance data output at the pluralityof post-operative timepoints after surgery, each value is at aparticular timepoint of the plurality of post-operative timepoints aftersurgery; wherein input data to train the at least one machine learningmodel comprises at least: (i) the pre-operative patient specific data,and (ii) the reconstruction plan data; instruct to display thereconstruction plan data and the predicted post-operative jointperformance data output at the plurality of post-operative timepointsafter surgery via a graphical user interface displayed on a displayassociated with a user on the display to the user; and update thepredicted post-operative joint performance data output determined fromthe at least one machine learning model in response to the user varyingany parameter of the reconstruction plan data that is then inputted intothe at least one machine learning model, before the arthroplastysurgery, during the arthroplasty surgery, or both.
 24. The system ofclaim 23, wherein the at least one processor is configured to receivethe pre-operative patient specific data by receiving the pre-operativepatient specific data over a communication network from at least oneelectronic medical resource.
 25. The system of claim 23, wherein the atleast one medical image comprises at least one of: (a) an X-ray image,(b) a computerized tomography image, (c) a magnetic resonance image, (d)a three-dimensional (3D) image, (e) a 3D medical image generated frommultiple X-ray images, (f) a frame of a video, or any combinationthereof.
 26. The system of claim 23, wherein the at least one predictedpost-operative joint performance data at the plurality of post-operativetimepoints after surgery are predicted for at least one of: (a) a numberof days, (b) a number of months, and (c) a number of years.
 27. Thesystem of claim 23, wherein the at least one processor is configured todisplay the predicted post-operative joint performance data output withrecommendations for the at least one arthroplasty surgical parameter.28. The system according to claim 23, wherein the joint is selected fromthe group consisting of a hip joint, a knee joint, a shoulder joint, anelbow joint, and an ankle joint.
 29. The system according to claim 23,wherein the joint is a shoulder joint.
 30. The system of claim 29,wherein the pre-operative patient specific data comprises: (a) patientdemographics, (b) a patient diagnosis, (c) a patient comorbidity, (d) apatient medical history, (e) a shoulder active range of motion measure,(f) a patient self-reported measure of pain, function, or both, (g) apatient score based on American Shoulder and Elbow Surgeons ShoulderScore (ASES), (h) a patient score based on Constant Shoulder Score(CSS), or any combination thereof.
 31. The system of claim 29, whereinthe at least one arthroplasty surgical procedure is selected from thegroup consisting of an anatomic total shoulder arthroplasty, a reversetotal shoulder arthroplasty, deltopectoral technique, and asuperior-lateral technique.
 32. The system of claim 29, wherein the atleast one predicted post-operative joint performance data at theplurality of post-operative timepoints after surgery are selected fromthe group consisting of an American Shoulder and Elbow (ASES) score, aUniversity of California, Los Angeles (UCLA) patient reported outcomemeasures score, a constant score, a global shoulder function score, aVisual Analogue Scale (VAS) Pain score, an abduction score, a forwardelevation score, and an external rotation score.
 33. The systemaccording to claim 23, wherein the at least one processor is furtherconfigured to determine from the at least one machine learning model, atleast one arthroplasty surgery recommendation to display to the user onthe display.
 34. The system according to claim 23, wherein the at leastone processor is further configured to: input the pre-operative patientspecific data to at least one second machine learning model to determinea second predicted post-operative joint performance data output at aplurality of second post-operative timepoints after surgery prior togenerating the reconstruction plan; wherein the at least one secondmachine learning model is trained to output data comprising a pluralityof second values for the second predicted post-operative jointperformance data output at the plurality of second post-operativetimepoints after surgery, each second value is at each particular secondtimepoint of the plurality of second post-operative timepoints aftersurgery; wherein input data to train the at least one second machinelearning model comprises at least the pre-operative patient specificdata; display the second predicted post-operative joint performance dataoutput on the display to the user as a displayed second predictedpost-operative joint performance data output; and wherein the at leastone processor is further configured to receive from the user, throughthe graphical user interface displayed on the display, the at least onearthroplasty surgical parameter based on the displayed second predictedpost-operative joint performance data output to generate thereconstruction plan.
 35. A method, comprising: receiving, by at leastone processor, pre-operative patient specific data for an arthroplastysurgery to be performed on a joint of a patient; wherein thepre-operative patient specific data comprises: (i) a medical history ofthe patient, (ii) a measured range of movement for at least one type ofjoint movement of the joint, and (iii) at least one pain metricassociated with the joint; receiving, by the at least one processor, atleast one medical image of the joint obtained from at least one medicalimaging procedure performed on the patient; receiving, by the at leastone processor, at least one arthroplasty surgical parameter; wherein theat least one arthroplasty surgical parameter is selected from: (i) atleast one implant, (ii) at least one implant size, (iii) at least onearthroplasty surgical procedure, (iv) at least one position forimplanting the at least one implant in the joint, or (v) any combinationthereof; generating, by the at least one processor, a reconstructionplan of the joint of the patient based at least in part on the at leastone medical image of the joint and the at least one arthroplastysurgical parameter; inputting, by the at least one processor, thepre-operative patient specific data and reconstruction plan data into atleast one machine learning model to determine a predicted post-operativejoint performance data output at a plurality of post-operativetimepoints after surgery; wherein the at least one machine learningmodel is trained to output data comprising a plurality of values for thepredicted post-operative joint performance data output at the pluralityof post-operative timepoints after surgery, each value is at aparticular timepoint of the plurality of post-operative timepoints aftersurgery; wherein input data to train the at least one machine learningmodel comprises at least: (i) the pre-operative patient specific data,and (ii) the reconstruction plan data; instructing, by the at least oneprocessor, to display the reconstruction plan data and the predictedpost-operative joint performance data output at the plurality ofpost-operative timepoints after surgery via a graphical user interfacedisplayed on a display associated with a user; and updating, by the atleast one processor, the predicted post-operative joint performance dataoutput determined from the at least one machine learning model inresponse to the user varying any parameter of the reconstruction plandata that is then inputted into the at least one machine learning model,before the arthroplasty surgery, during the arthroplasty surgery, orboth.
 36. The method of claim 35, wherein receiving the pre-operativepatient specific data comprises receiving the pre-operative patientspecific data over a communication network from at least one electronicmedical resource.
 37. The method of claim 35, wherein the at least onemedical image comprises at least one of: (a) an X-ray image, (b) acomputerized tomography image, (c) a magnetic resonance image, (d) athree-dimensional (3D) image, (e) a 3D medical image generated frommultiple X-ray images, (f) a frame of a video, or any combinationthereof.
 38. The method of claim 35, wherein the at least one predictedpost-operative joint performance data at the plurality of post-operativetimepoints after surgery are predicted for at least one of: (a) a numberof days, (b) a number of months, and (c) a number of years.
 39. Themethod of claim 35, wherein displaying the predicted post-operativejoint performance data output comprises displaying the predictedpost-operative joint performance data output with recommendations forthe at least one arthroplasty surgical parameter.
 40. The method ofclaim 35, wherein the joint is selected from the group consisting of ahip joint, a knee joint, a shoulder joint, an elbow j oint, and an anklejoint.
 41. The method of claim 35, wherein the joint is a shoulderjoint.
 42. The method of claim 41, wherein the pre-operative patientspecific data comprises: (a) patient demographics, (b) a patientdiagnosis, (c) a patient comorbidity, (d) a patient medical history, (e)a shoulder active range of motion measure, (f) a patient self-reportedmeasure of pain, function, or both, (g) a patient score based onAmerican Shoulder and Elbow Surgeons Shoulder Score (ASES), (h) apatient score based on Constant Shoulder Score (CSS), or any combinationthereof.
 43. The method of claim 41, wherein the at least onearthroplasty surgical procedure is selected from the group consisting ofan anatomic total shoulder arthroplasty, a reverse total shoulderarthroplasty, deltopectoral technique, and a superior-lateral technique.44. The method of claim 41, wherein the at least one predictedpost-operative joint performance data at the plurality of post-operativetimepoints after surgery are selected from the group consisting of anAmerican Shoulder and Elbow (ASES) score, a University of California,Los Angeles (UCLA) patient reported outcome measures score, a constantscore, a global shoulder function score, a Visual Analogue Scale (VAS)Pain score, an abduction score, a forward elevation score, and anexternal rotation score.
 45. The method according to claim 35, furthercomprising determining, by the at least one processor, from the at leastone machine learning model, at least one arthroplasty surgeryrecommendation to display to the user on the display.
 46. The methodaccording to claim 35, further comprising inputting, by the at least oneprocessor, the pre-operative patient specific data to at least onesecond machine learning model to determine a second predictedpost-operative joint performance data output at a plurality of secondpost-operative timepoints after surgery prior to generating thereconstruction plan; wherein the at least one second machine learningmodel is trained to output data comprising a plurality of second valuesfor the second predicted post-operative joint performance data output atthe plurality of second post-operative timepoints after surgery, eachsecond value is at each particular second timepoint of the plurality ofsecond post-operative timepoints after surgery; wherein input data totrain the at least one second machine learning model comprises at leastthe pre-operative patient specific data; displaying, by the at least oneprocessor, the second predicted post-operative joint performance dataoutput on the display to the user as a displayed second predictedpost-operative joint performance data output; and wherein the receivingfrom the user the at least one arthroplasty surgical parameter comprisesreceiving through the graphical user interface displayed on the display,the at least one arthroplasty surgical parameter based on the displayedsecond predicted post-operative joint performance data output forgenerating the reconstruction plan.